AAL_Sales_Analysis.ipynb

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        "<a href=\"https://colab.research.google.com/github/lorettarehm/AIML/blob/main/AAL_Sales_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Import Library\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
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      "cell_type": "code",
      "source": [
        "# Load the data\n",
        "data = pd.read_csv('AusApparalSales4thQrt2020.csv')"
      ],
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      "source": [
        "## 1. Data Wrangling"
      ],
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      "cell_type": "markdown",
      "source": [
        "##### a. check for missing values"
      ],
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      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
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              "Date     0\n",
              "Time     0\n",
              "State    0\n",
              "Group    0\n",
              "Unit     0\n",
              "Sales    0\n",
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      "source": [
        "There is no missing values in this data. If there were missing values , we could :\n"
      ],
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      "cell_type": "markdown",
      "source": [
        "> * Drop rows with missing values : data.dropna()\n",
        "\n",
        "> * Fill missing values : mean , median, mode data.fillna()"
      ],
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        "b. get summary statistics of data"
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              "              Unit          Sales\n",
              "count  7560.000000    7560.000000\n",
              "mean     18.005423   45013.558201\n",
              "std      12.901403   32253.506944\n",
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              "25%       8.000000   20000.000000\n",
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    {
      "cell_type": "markdown",
      "source": [
        "##### comment"
      ],
      "metadata": {
        "id": "qbjtZj79JCok"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "#### b. Data Normalization ( Min-Max Scaling )"
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        "id": "w-Djqh2QNyWZ"
      }
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      "cell_type": "code",
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        "data.head()"
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              "         Date        Time State     Group  Unit  Sales\n",
              "0  1-Oct-2020     Morning    WA      Kids     8  20000\n",
              "1  1-Oct-2020     Morning    WA       Men     8  20000\n",
              "2  1-Oct-2020     Morning    WA     Women     4  10000\n",
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              "4  1-Oct-2020   Afternoon    WA      Kids     3   7500"
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              "      <td>15</td>\n",
              "      <td>37500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1-Oct-2020</td>\n",
              "      <td>Afternoon</td>\n",
              "      <td>WA</td>\n",
              "      <td>Kids</td>\n",
              "      <td>3</td>\n",
              "      <td>7500</td>\n",
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              "        document.querySelector('#df-f252f6c8-07d4-46e9-aabd-7e954d3b0c82 button.colab-df-convert');\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "</svg>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "  [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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              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    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-fb1f825c-c12a-419a-9387-2d9f650c1872 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 7560,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 90,\n        \"samples\": [\n          \"11-Nov-2020\",\n          \"23-Oct-2020\",\n          \"26-Nov-2020\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \" Morning\",\n          \" Afternoon\",\n          \" Evening\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \" WA\",\n          \" NT\",\n          \" NSW\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \" Men\",\n          \" Seniors\",\n          \" Kids\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 2,\n        \"max\": 65,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          52,\n          54,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32253,\n        \"min\": 5000,\n        \"max\": 162500,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          130000,\n          135000,\n          20000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "Mjlc_76MJAYq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()"
      ],
      "metadata": {
        "id": "Dpo2926ROOMA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[['Unit', 'Sales']]= scaler.fit_transform(data[['Unit', 'Sales']])"
      ],
      "metadata": {
        "id": "gbDmUAsmN8bF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ByBQ95f9Oplt",
        "outputId": "61bc45e9-1407-41c7-e3a2-b2f03bbcec24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Date        Time State     Group      Unit     Sales\n",
              "0      1-Oct-2020     Morning    WA      Kids  0.095238  0.095238\n",
              "1      1-Oct-2020     Morning    WA       Men  0.095238  0.095238\n",
              "2      1-Oct-2020     Morning    WA     Women  0.031746  0.031746\n",
              "3      1-Oct-2020     Morning    WA   Seniors  0.206349  0.206349\n",
              "4      1-Oct-2020   Afternoon    WA      Kids  0.015873  0.015873\n",
              "...           ...         ...   ...       ...       ...       ...\n",
              "7555  30-Dec-2020   Afternoon   TAS   Seniors  0.190476  0.190476\n",
              "7556  30-Dec-2020     Evening   TAS      Kids  0.206349  0.206349\n",
              "7557  30-Dec-2020     Evening   TAS       Men  0.206349  0.206349\n",
              "7558  30-Dec-2020     Evening   TAS     Women  0.142857  0.142857\n",
              "7559  30-Dec-2020     Evening   TAS   Seniors  0.174603  0.174603\n",
              "\n",
              "[7560 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-34126d92-58b9-48b0-8017-46ea4343ab85\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>State</th>\n",
              "      <th>Group</th>\n",
              "      <th>Unit</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1-Oct-2020</td>\n",
              "      <td>Morning</td>\n",
              "      <td>WA</td>\n",
              "      <td>Kids</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>0.095238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1-Oct-2020</td>\n",
              "      <td>Morning</td>\n",
              "      <td>WA</td>\n",
              "      <td>Men</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>0.095238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1-Oct-2020</td>\n",
              "      <td>Morning</td>\n",
              "      <td>WA</td>\n",
              "      <td>Women</td>\n",
              "      <td>0.031746</td>\n",
              "      <td>0.031746</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1-Oct-2020</td>\n",
              "      <td>Morning</td>\n",
              "      <td>WA</td>\n",
              "      <td>Seniors</td>\n",
              "      <td>0.206349</td>\n",
              "      <td>0.206349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1-Oct-2020</td>\n",
              "      <td>Afternoon</td>\n",
              "      <td>WA</td>\n",
              "      <td>Kids</td>\n",
              "      <td>0.015873</td>\n",
              "      <td>0.015873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7555</th>\n",
              "      <td>30-Dec-2020</td>\n",
              "      <td>Afternoon</td>\n",
              "      <td>TAS</td>\n",
              "      <td>Seniors</td>\n",
              "      <td>0.190476</td>\n",
              "      <td>0.190476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7556</th>\n",
              "      <td>30-Dec-2020</td>\n",
              "      <td>Evening</td>\n",
              "      <td>TAS</td>\n",
              "      <td>Kids</td>\n",
              "      <td>0.206349</td>\n",
              "      <td>0.206349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7557</th>\n",
              "      <td>30-Dec-2020</td>\n",
              "      <td>Evening</td>\n",
              "      <td>TAS</td>\n",
              "      <td>Men</td>\n",
              "      <td>0.206349</td>\n",
              "      <td>0.206349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7558</th>\n",
              "      <td>30-Dec-2020</td>\n",
              "      <td>Evening</td>\n",
              "      <td>TAS</td>\n",
              "      <td>Women</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7559</th>\n",
              "      <td>30-Dec-2020</td>\n",
              "      <td>Evening</td>\n",
              "      <td>TAS</td>\n",
              "      <td>Seniors</td>\n",
              "      <td>0.174603</td>\n",
              "      <td>0.174603</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7560 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-34126d92-58b9-48b0-8017-46ea4343ab85')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-34126d92-58b9-48b0-8017-46ea4343ab85 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-34126d92-58b9-48b0-8017-46ea4343ab85');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "        height: 32px;\n",
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              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 7560,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 90,\n        \"samples\": [\n          \"11-Nov-2020\",\n          \"23-Oct-2020\",\n          \"26-Nov-2020\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \" Morning\",\n          \" Afternoon\",\n          \" Evening\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \" WA\",\n          \" NT\",\n          \" NSW\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \" Men\",\n          \" Seniors\",\n          \" Kids\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20478417107280045,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          0.7936507936507936,\n          0.8253968253968254,\n          0.09523809523809523\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20478417107280047,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          0.7936507936507936,\n          0.8253968253968255,\n          0.09523809523809523\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Data Analysis"
      ],
      "metadata": {
        "id": "xhPxvcosRBdz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Identifying higest and lowest revenue generating states"
      ],
      "metadata": {
        "id": "phefvuKwRXoH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby('State')['Sales'].sum().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "Rt-Mg3q8Oqds",
        "outputId": "2915332e-e75e-4757-e82a-19209c1fd890"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "State\n",
              " VIC    635.968254\n",
              " NSW    441.714286\n",
              " SA     339.412698\n",
              " QLD    177.888889\n",
              " TAS    110.222222\n",
              " NT     109.079365\n",
              " WA     106.365079\n",
              "Name: Sales, dtype: float64"
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              "      <td>109.079365</td>\n",
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              "</div><br><label><b>dtype:</b> float64</label>"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "data.groupby('Group')['Sales'].sum().sort_values(ascending=False)"
      ],
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          "height": 241
        },
        "id": "GxhloeaeT7xm",
        "outputId": "c47d77bd-b52f-4f00-f6ba-97404fc2348b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Group\n",
              " Men        484.444444\n",
              " Women      482.492063\n",
              " Kids       480.142857\n",
              " Seniors    473.571429\n",
              "Name: Sales, dtype: float64"
            ],
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "data['Date'] = pd.to_datetime(data['Date'])"
      ],
      "metadata": {
        "id": "IMebb_4PUMof"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "data['Week']=data['Date'].dt.isocalendar().week"
      ],
      "metadata": {
        "id": "Ieoz56gVUZvg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Month']=data['Date'].dt.month"
      ],
      "metadata": {
        "id": "OCF4Oxp4Vt_N"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "weekly_sales =data.groupby('Week')['Sales'].sum().sort_values(ascending=False)"
      ],
      "metadata": {
        "id": "r0lKSeTAV2oB"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "monthly_sales =data.groupby('Month')['Sales'].sum().sort_values(ascending=False)"
      ],
      "metadata": {
        "id": "Q13lFtWqWMtC"
      },
      "execution_count": null,
      "outputs": []
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    {
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        "data['Sales'].sum()"
      ],
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        },
        "id": "c8bT4EqmWVI8",
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          "data": {
            "text/plain": [
              "1920.6507936507937"
            ]
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date        Time State     Group      Unit     Sales  Week  Month\n",
              "0    2020-10-01     Morning    WA      Kids  0.095238  0.095238    40     10\n",
              "1    2020-10-01     Morning    WA       Men  0.095238  0.095238    40     10\n",
              "2    2020-10-01     Morning    WA     Women  0.031746  0.031746    40     10\n",
              "3    2020-10-01     Morning    WA   Seniors  0.206349  0.206349    40     10\n",
              "4    2020-10-01   Afternoon    WA      Kids  0.015873  0.015873    40     10\n",
              "...         ...         ...   ...       ...       ...       ...   ...    ...\n",
              "7555 2020-12-30   Afternoon   TAS   Seniors  0.190476  0.190476    53     12\n",
              "7556 2020-12-30     Evening   TAS      Kids  0.206349  0.206349    53     12\n",
              "7557 2020-12-30     Evening   TAS       Men  0.206349  0.206349    53     12\n",
              "7558 2020-12-30     Evening   TAS     Women  0.142857  0.142857    53     12\n",
              "7559 2020-12-30     Evening   TAS   Seniors  0.174603  0.174603    53     12\n",
              "\n",
              "[7560 rows x 8 columns]"
            ],
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              "      <td>WA</td>\n",
              "      <td>Kids</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>40</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-10-01</td>\n",
              "      <td>Morning</td>\n",
              "      <td>WA</td>\n",
              "      <td>Men</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>0.095238</td>\n",
              "      <td>40</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-10-01</td>\n",
              "      <td>Morning</td>\n",
              "      <td>WA</td>\n",
              "      <td>Women</td>\n",
              "      <td>0.031746</td>\n",
              "      <td>0.031746</td>\n",
              "      <td>40</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-10-01</td>\n",
              "      <td>Morning</td>\n",
              "      <td>WA</td>\n",
              "      <td>Seniors</td>\n",
              "      <td>0.206349</td>\n",
              "      <td>0.206349</td>\n",
              "      <td>40</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-10-01</td>\n",
              "      <td>Afternoon</td>\n",
              "      <td>WA</td>\n",
              "      <td>Kids</td>\n",
              "      <td>0.015873</td>\n",
              "      <td>0.015873</td>\n",
              "      <td>40</td>\n",
              "      <td>10</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7555</th>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>Afternoon</td>\n",
              "      <td>TAS</td>\n",
              "      <td>Seniors</td>\n",
              "      <td>0.190476</td>\n",
              "      <td>0.190476</td>\n",
              "      <td>53</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7556</th>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>Evening</td>\n",
              "      <td>TAS</td>\n",
              "      <td>Kids</td>\n",
              "      <td>0.206349</td>\n",
              "      <td>0.206349</td>\n",
              "      <td>53</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7557</th>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>Evening</td>\n",
              "      <td>TAS</td>\n",
              "      <td>Men</td>\n",
              "      <td>0.206349</td>\n",
              "      <td>0.206349</td>\n",
              "      <td>53</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7558</th>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>Evening</td>\n",
              "      <td>TAS</td>\n",
              "      <td>Women</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>53</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7559</th>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>Evening</td>\n",
              "      <td>TAS</td>\n",
              "      <td>Seniors</td>\n",
              "      <td>0.174603</td>\n",
              "      <td>0.174603</td>\n",
              "      <td>53</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7560 rows × 8 columns</p>\n",
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              "\n",
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              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 7560,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2020-10-01 00:00:00\",\n        \"max\": \"2020-12-30 00:00:00\",\n        \"num_unique_values\": 90,\n        \"samples\": [\n          \"2020-11-11 00:00:00\",\n          \"2020-10-23 00:00:00\",\n          \"2020-11-26 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \" Morning\",\n          \" Afternoon\",\n          \" Evening\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \" WA\",\n          \" NT\",\n          \" NSW\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \" Men\",\n          \" Seniors\",\n          \" Kids\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Unit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20478417107280045,\n        \"min\": 0.0,\n        \"max\": 0.9999999999999999,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          0.7936507936507936,\n          0.8253968253968254,\n          0.09523809523809523\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.20478417107280047,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          0.7936507936507936,\n          0.8253968253968255,\n          0.09523809523809523\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Week\",\n      \"properties\": {\n        \"dtype\": \"UInt32\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          49,\n          51,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10,\n          11,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cTqBxNpcWijx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data Visulaization"
      ],
      "metadata": {
        "id": "szOC1j1AXD6w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "a.  State wise sales Analysis for Demgraphic data ( group , sales , state)"
      ],
      "metadata": {
        "id": "RoyHr6u1XHWi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "b. Group wise sales Analysis across State"
      ],
      "metadata": {
        "id": "MngPG1UyXbAr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "c. Timewise , time of day analysis"
      ],
      "metadata": {
        "id": "So14Oz3FYBzL"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Lbc5_kW8XG3z"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}