Basic statitistics-1 Assignment.ipynb

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        "import pandas as pd #importing the required libraries\n",
        "import numpy as np\n",
        "import plotly.express as px\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
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        "data = pd.read_csv(\"/content/sales_data_with_discounts.csv\") #load the dataset into environment\n",
        "data.head(11)"
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              "          Date       Day  SKU City  Volume       BU    Brand        Model  \\\n",
              "0   01-04-2021  Thursday  M01    C      15  Mobiles    RealU        RU-10   \n",
              "1   01-04-2021  Thursday  M02    C      10  Mobiles    RealU    RU-9 Plus   \n",
              "2   01-04-2021  Thursday  M03    C       7  Mobiles     YouM        YM-99   \n",
              "3   01-04-2021  Thursday  M04    C       6  Mobiles     YouM   YM-99 Plus   \n",
              "4   01-04-2021  Thursday  M05    C       3  Mobiles     YouM       YM-98    \n",
              "5   01-04-2021  Thursday  M06    C       3  Mobiles    RealU         RU-9   \n",
              "6   01-04-2021  Thursday  M07    C       3  Mobiles  Sumsang         S-20   \n",
              "7   01-04-2021  Thursday  M08    C       2  Mobiles  Sumsang         S-21   \n",
              "8   01-04-2021  Thursday  M09    C       2  Mobiles   Orange         O-10   \n",
              "9   01-04-2021  Thursday  M10    C       3  Mobiles   Orange         O-11   \n",
              "10  01-04-2021  Thursday  F01    C      13     FMCG   Babaji  Babaji Oil    \n",
              "\n",
              "    Avg Price  Total Sales Value  Discount Rate (%)  Discount Amount  \\\n",
              "0       12100             181500          11.654820     21153.498820   \n",
              "1       10100             101000          11.560498     11676.102961   \n",
              "2       16100             112700           9.456886     10657.910157   \n",
              "3       20100             120600           6.935385      8364.074702   \n",
              "4        8100              24300          17.995663      4372.946230   \n",
              "5        8100              24300          16.996489      4130.146805   \n",
              "6       49100             147300           9.228812     13594.039719   \n",
              "7       54100             108200           5.553719      6009.124321   \n",
              "8       55100             110200           7.410104      8165.935144   \n",
              "9       60100             180300           9.928444     17900.983733   \n",
              "10        400               5200          18.869242       981.200566   \n",
              "\n",
              "    Net Sales Value  \n",
              "0     160346.501180  \n",
              "1      89323.897039  \n",
              "2     102042.089843  \n",
              "3     112235.925298  \n",
              "4      19927.053770  \n",
              "5      20169.853195  \n",
              "6     133705.960281  \n",
              "7     102190.875679  \n",
              "8     102034.064856  \n",
              "9     162399.016267  \n",
              "10      4218.799434  "
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              "      <td>01-04-2021</td>\n",
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              "      <td>Thursday</td>\n",
              "      <td>M05</td>\n",
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              "      <td>Mobiles</td>\n",
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              "      <td>M06</td>\n",
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              "  <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",
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              "      quickchartButtonEl.style.display =\n",
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              "    })();\n",
              "  </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\": 450,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"10-04-2021\",\n          \"12-04-2021\",\n          \"01-04-2021\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Day\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Thursday\",\n          \"Friday\",\n          \"Tuesday\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SKU\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"L08\",\n          \"F06\",\n          \"L04\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 1,\n        \"max\": 31,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BU\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Mobiles\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Jeera\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"W-Lounge\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Avg Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18079,\n        \"min\": 290,\n        \"max\": 60100,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          60100\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Sales Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50535,\n        \"min\": 400,\n        \"max\": 196400,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          3000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Discount Rate (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.22060215332551,\n        \"min\": 5.007822189204133,\n        \"max\": 19.99240704193018,\n        \"num_unique_values\": 450,\n        \"samples\": [\n          18.99129131753696\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Discount Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4509.902963353998,\n        \"min\": 69.17794228822787,\n        \"max\": 25738.02219375868,\n        \"num_unique_values\": 450,\n        \"samples\": [\n          151.9303305402957\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Net Sales Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46358.65662406695,\n        \"min\": 326.97480055470817,\n        \"max\": 179507.47904902254,\n        \"num_unique_values\": 450,\n        \"samples\": [\n          648.0696694597043\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Descriptive Analytics for Numerical Columns"
      ],
      "metadata": {
        "id": "d7n_rQBzORK3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.info() #To get the information obout data"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-20T17:08:37.112828Z",
          "iopub.execute_input": "2024-07-20T17:08:37.113215Z",
          "iopub.status.idle": "2024-07-20T17:08:37.141462Z",
          "shell.execute_reply.started": "2024-07-20T17:08:37.113187Z",
          "shell.execute_reply": "2024-07-20T17:08:37.140043Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q4zJ8uT3ORK5",
        "outputId": "dd20f657-9b81-4887-9e8b-29fc1a365d8e"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 450 entries, 0 to 449\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   Date               450 non-null    object \n",
            " 1   Day                450 non-null    object \n",
            " 2   SKU                450 non-null    object \n",
            " 3   City               450 non-null    object \n",
            " 4   Volume             450 non-null    int64  \n",
            " 5   BU                 450 non-null    object \n",
            " 6   Brand              450 non-null    object \n",
            " 7   Model              450 non-null    object \n",
            " 8   Avg Price          450 non-null    int64  \n",
            " 9   Total Sales Value  450 non-null    int64  \n",
            " 10  Discount Rate (%)  450 non-null    float64\n",
            " 11  Discount Amount    450 non-null    float64\n",
            " 12  Net Sales Value    450 non-null    float64\n",
            "dtypes: float64(3), int64(3), object(7)\n",
            "memory usage: 45.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "By considering the information of the dataset, I conclude that Volume, Avg Price, Total Sales Value, Discount Rate (%),\tDiscount Amount,\tNet Sales Value are the numerical Columns ."
      ],
      "metadata": {
        "id": "Y8rU9G4wORK5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe() #To get the Statistical informarion of data"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-20T17:15:40.355873Z",
          "iopub.execute_input": "2024-07-20T17:15:40.356624Z",
          "iopub.status.idle": "2024-07-20T17:15:40.395619Z",
          "shell.execute_reply.started": "2024-07-20T17:15:40.356590Z",
          "shell.execute_reply": "2024-07-20T17:15:40.394573Z"
        },
        "trusted": true,
        "id": "Bsm18k8iORK6",
        "outputId": "9a3c2da7-49ba-4464-c2ce-84f3a08eb982",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Volume     Avg Price  Total Sales Value  Discount Rate (%)  \\\n",
              "count  450.000000    450.000000         450.000000         450.000000   \n",
              "mean     5.066667  10453.433333       33812.835556          15.155242   \n",
              "std      4.231602  18079.904840       50535.074173           4.220602   \n",
              "min      1.000000    290.000000         400.000000           5.007822   \n",
              "25%      3.000000    465.000000        2700.000000          13.965063   \n",
              "50%      4.000000   1450.000000        5700.000000          16.577766   \n",
              "75%      6.000000  10100.000000       53200.000000          18.114718   \n",
              "max     31.000000  60100.000000      196400.000000          19.992407   \n",
              "\n",
              "       Discount Amount  Net Sales Value  \n",
              "count       450.000000       450.000000  \n",
              "mean       3346.499424     30466.336131  \n",
              "std        4509.902963     46358.656624  \n",
              "min          69.177942       326.974801  \n",
              "25%         460.459304      2202.208645  \n",
              "50%         988.933733      4677.788059  \n",
              "75%        5316.495427     47847.912852  \n",
              "max       25738.022194    179507.479049  "
            ],
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              "      <th>Volume</th>\n",
              "      <th>Avg Price</th>\n",
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              "      <td>450.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
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              "      <th>25%</th>\n",
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              "      <td>2700.000000</td>\n",
              "      <td>13.965063</td>\n",
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              "      <td>2202.208645</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>1450.000000</td>\n",
              "      <td>5700.000000</td>\n",
              "      <td>16.577766</td>\n",
              "      <td>988.933733</td>\n",
              "      <td>4677.788059</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>10100.000000</td>\n",
              "      <td>53200.000000</td>\n",
              "      <td>18.114718</td>\n",
              "      <td>5316.495427</td>\n",
              "      <td>47847.912852</td>\n",
              "    </tr>\n",
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              "      <td>196400.000000</td>\n",
              "      <td>19.992407</td>\n",
              "      <td>25738.022194</td>\n",
              "      <td>179507.479049</td>\n",
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              "      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-6b4ce5c9-a132-4ec3-8664-ece5e526b669 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156.65110667068927,\n        \"min\": 1.0,\n        \"max\": 450.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.066666666666666,\n          4.0,\n          450.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Avg Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20240.811540701754,\n        \"min\": 290.0,\n        \"max\": 60100.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          10453.433333333332,\n          1450.0,\n          450.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Sales Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65919.15331321131,\n        \"min\": 400.0,\n        \"max\": 196400.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          33812.83555555555,\n          5700.0,\n          450.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Discount Rate (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 154.50823774782953,\n        \"min\": 4.22060215332551,\n        \"max\": 450.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15.155241895330914,\n          16.57776564929597,\n          450.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Discount Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8575.337507445172,\n        \"min\": 69.17794228822787,\n        \"max\": 25738.02219375868,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3346.499424342415,\n          988.9337332382784,\n          450.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Net Sales Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60310.39264790412,\n        \"min\": 326.97480055470817,\n        \"max\": 179507.47904902254,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          30466.336131270138,\n          4677.788059301853,\n          450.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe().T[['mean','std','50%']] #Display the mean, standard deviation and median of the data ."
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-20T17:16:19.128061Z",
          "iopub.execute_input": "2024-07-20T17:16:19.129022Z",
          "iopub.status.idle": "2024-07-20T17:16:19.159615Z",
          "shell.execute_reply.started": "2024-07-20T17:16:19.128988Z",
          "shell.execute_reply": "2024-07-20T17:16:19.158427Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "UnWR40L7ORK6",
        "outputId": "d91f073c-5efe-43ec-9eee-061307afed44"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           mean           std          50%\n",
              "Volume                 5.066667      4.231602     4.000000\n",
              "Avg Price          10453.433333  18079.904840  1450.000000\n",
              "Total Sales Value  33812.835556  50535.074173  5700.000000\n",
              "Discount Rate (%)     15.155242      4.220602    16.577766\n",
              "Discount Amount     3346.499424   4509.902963   988.933733\n",
              "Net Sales Value    30466.336131  46358.656624  4677.788059"
            ],
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              "\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>Volume</th>\n",
              "      <td>5.066667</td>\n",
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              "      <td>4.000000</td>\n",
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              "      <td>18079.904840</td>\n",
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              "    </tr>\n",
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              "      <th>Total Sales Value</th>\n",
              "      <td>33812.835556</td>\n",
              "      <td>50535.074173</td>\n",
              "      <td>5700.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount Rate (%)</th>\n",
              "      <td>15.155242</td>\n",
              "      <td>4.220602</td>\n",
              "      <td>16.577766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount Amount</th>\n",
              "      <td>3346.499424</td>\n",
              "      <td>4509.902963</td>\n",
              "      <td>988.933733</td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "    </g>\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",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-80e81c22-4e57-4c38-8969-6fcfe32620aa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "\n",
              "    </div>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15332.28579375928,\n        \"min\": 5.066666666666666,\n        \"max\": 33812.83555555555,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5.066666666666666,\n          10453.433333333332,\n          30466.336131270138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23110.250282900754,\n        \"min\": 4.22060215332551,\n        \"max\": 50535.07417255325,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.231602391213923,\n          18079.90483993647,\n          46358.65662406695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2448.936036500305,\n        \"min\": 4.0,\n        \"max\": 5700.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.0,\n          1450.0,\n          4677.788059301853\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = data.describe().T[['mean','50%','std']]\n",
        "df.rename(columns={'50%':'Median','mean':'Mean','std':'Standard Deviation'},inplace=True) #rename the columns names\n",
        "df1 = df\n",
        "df1"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-20T17:16:19.315681Z",
          "iopub.execute_input": "2024-07-20T17:16:19.316905Z",
          "iopub.status.idle": "2024-07-20T17:16:19.350143Z",
          "shell.execute_reply.started": "2024-07-20T17:16:19.316862Z",
          "shell.execute_reply": "2024-07-20T17:16:19.349196Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "vrypOaNkORK7",
        "outputId": "b412087e-ae3f-4234-c56b-e5512d890f61"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           Mean       Median  Standard Deviation\n",
              "Volume                 5.066667     4.000000            4.231602\n",
              "Avg Price          10453.433333  1450.000000        18079.904840\n",
              "Total Sales Value  33812.835556  5700.000000        50535.074173\n",
              "Discount Rate (%)     15.155242    16.577766            4.220602\n",
              "Discount Amount     3346.499424   988.933733         4509.902963\n",
              "Net Sales Value    30466.336131  4677.788059        46358.656624"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Mean</th>\n",
              "      <th>Median</th>\n",
              "      <th>Standard Deviation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Volume</th>\n",
              "      <td>5.066667</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.231602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Avg Price</th>\n",
              "      <td>10453.433333</td>\n",
              "      <td>1450.000000</td>\n",
              "      <td>18079.904840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total Sales Value</th>\n",
              "      <td>33812.835556</td>\n",
              "      <td>5700.000000</td>\n",
              "      <td>50535.074173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount Rate (%)</th>\n",
              "      <td>15.155242</td>\n",
              "      <td>16.577766</td>\n",
              "      <td>4.220602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount Amount</th>\n",
              "      <td>3346.499424</td>\n",
              "      <td>988.933733</td>\n",
              "      <td>4509.902963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Net Sales Value</th>\n",
              "      <td>30466.336131</td>\n",
              "      <td>4677.788059</td>\n",
              "      <td>46358.656624</td>\n",
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              "      spin 1s steps(1) infinite;\n",
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              "  }\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15332.28579375928,\n        \"min\": 5.066666666666666,\n        \"max\": 33812.83555555555,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5.066666666666666,\n          10453.433333333332,\n          30466.336131270138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Median\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2448.936036500305,\n        \"min\": 4.0,\n        \"max\": 5700.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.0,\n          1450.0,\n          4677.788059301853\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Standard Deviation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23110.250282900754,\n        \"min\": 4.22060215332551,\n        \"max\": 50535.07417255325,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.231602391213923,\n          18079.90483993647,\n          46358.65662406695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in data.describe().columns:\n",
        "    print(f\"\"\"Mode of {i}:\n",
        "          {data[i].mode()}\\n\"\"\") #Find and Display the mode of every colummn"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-20T17:21:50.835613Z",
          "iopub.execute_input": "2024-07-20T17:21:50.836162Z",
          "iopub.status.idle": "2024-07-20T17:21:50.867431Z",
          "shell.execute_reply.started": "2024-07-20T17:21:50.836126Z",
          "shell.execute_reply": "2024-07-20T17:21:50.866324Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BaYR-UwrORK7",
        "outputId": "20c05a19-e3dc-46a1-ee42-46229dca0be1"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mode of Volume:\n",
            "          0    3\n",
            "Name: Volume, dtype: int64\n",
            "\n",
            "Mode of Avg Price:\n",
            "          0     400\n",
            "1     450\n",
            "2     500\n",
            "3    1300\n",
            "4    8100\n",
            "Name: Avg Price, dtype: int64\n",
            "\n",
            "Mode of Total Sales Value:\n",
            "          0    24300\n",
            "Name: Total Sales Value, dtype: int64\n",
            "\n",
            "Mode of Discount Rate (%):\n",
            "          0       5.007822\n",
            "1       5.055218\n",
            "2       5.059801\n",
            "3       5.072124\n",
            "4       5.084108\n",
            "         ...    \n",
            "445    19.940610\n",
            "446    19.965340\n",
            "447    19.976879\n",
            "448    19.982257\n",
            "449    19.992407\n",
            "Name: Discount Rate (%), Length: 450, dtype: float64\n",
            "\n",
            "Mode of Discount Amount:\n",
            "          0         69.177942\n",
            "1         73.025199\n",
            "2         93.649161\n",
            "3         94.682735\n",
            "4        102.705757\n",
            "           ...     \n",
            "445    17900.983733\n",
            "446    21153.498820\n",
            "447    21496.675367\n",
            "448    25328.224204\n",
            "449    25738.022194\n",
            "Name: Discount Amount, Length: 450, dtype: float64\n",
            "\n",
            "Mode of Net Sales Value:\n",
            "          0         326.974801\n",
            "1         330.822058\n",
            "2         466.350839\n",
            "3         485.317265\n",
            "4         496.607104\n",
            "           ...      \n",
            "445    162603.186379\n",
            "446    163915.971000\n",
            "447    163967.080070\n",
            "448    166263.161348\n",
            "449    179507.479049\n",
            "Name: Net Sales Value, Length: 450, dtype: float64\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.insert(3,\"Mode\",[3,[400,450,500,1300,8100],24300,17.51,735.71,'None'],True) #inserting a new columns to table\n",
        "df #display mean , standard deviation , median , mode of data"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-20T17:21:57.463307Z",
          "iopub.execute_input": "2024-07-20T17:21:57.463711Z",
          "iopub.status.idle": "2024-07-20T17:21:57.480413Z",
          "shell.execute_reply.started": "2024-07-20T17:21:57.463675Z",
          "shell.execute_reply": "2024-07-20T17:21:57.479397Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "81nxrfZ9ORK8",
        "outputId": "a5210be3-655b-493f-f184-63f5f048ac6a"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           Mean       Median  Standard Deviation  \\\n",
              "Volume                 5.066667     4.000000            4.231602   \n",
              "Avg Price          10453.433333  1450.000000        18079.904840   \n",
              "Total Sales Value  33812.835556  5700.000000        50535.074173   \n",
              "Discount Rate (%)     15.155242    16.577766            4.220602   \n",
              "Discount Amount     3346.499424   988.933733         4509.902963   \n",
              "Net Sales Value    30466.336131  4677.788059        46358.656624   \n",
              "\n",
              "                                          Mode  \n",
              "Volume                                       3  \n",
              "Avg Price          [400, 450, 500, 1300, 8100]  \n",
              "Total Sales Value                        24300  \n",
              "Discount Rate (%)                        17.51  \n",
              "Discount Amount                         735.71  \n",
              "Net Sales Value                           None  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-79c18153-cf3b-4070-8058-0fde18913db9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Mean</th>\n",
              "      <th>Median</th>\n",
              "      <th>Standard Deviation</th>\n",
              "      <th>Mode</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Volume</th>\n",
              "      <td>5.066667</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.231602</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Avg Price</th>\n",
              "      <td>10453.433333</td>\n",
              "      <td>1450.000000</td>\n",
              "      <td>18079.904840</td>\n",
              "      <td>[400, 450, 500, 1300, 8100]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total Sales Value</th>\n",
              "      <td>33812.835556</td>\n",
              "      <td>5700.000000</td>\n",
              "      <td>50535.074173</td>\n",
              "      <td>24300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount Rate (%)</th>\n",
              "      <td>15.155242</td>\n",
              "      <td>16.577766</td>\n",
              "      <td>4.220602</td>\n",
              "      <td>17.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount Amount</th>\n",
              "      <td>3346.499424</td>\n",
              "      <td>988.933733</td>\n",
              "      <td>4509.902963</td>\n",
              "      <td>735.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Net Sales Value</th>\n",
              "      <td>30466.336131</td>\n",
              "      <td>4677.788059</td>\n",
              "      <td>46358.656624</td>\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15332.28579375928,\n        \"min\": 5.066666666666666,\n        \"max\": 33812.83555555555,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5.066666666666666,\n          10453.433333333332,\n          30466.336131270138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Median\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2448.936036500305,\n        \"min\": 4.0,\n        \"max\": 5700.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.0,\n          1450.0,\n          4677.788059301853\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Standard Deviation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23110.250282900754,\n        \"min\": 4.22060215332551,\n        \"max\": 50535.07417255325,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.231602391213923,\n          18079.90483993647,\n          46358.65662406695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mode\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Volume:** The average volume is low at 5.07 with a small standard deviation, indicating consistent low volume sales across the dataset.\n",
        "\n",
        "**Avg Price:** There is a significant difference between the mean and median prices, suggesting a skewed distribution with some very high values inflating the average.\n",
        "\n",
        "**Total Sales Value:** Similar to the average price, the high standard deviation and difference between the mean and median indicate a few very high sales values.\n",
        "\n",
        "**Discount Rate (%):** The average discount rate is around 15%, with a relatively low standard deviation, suggesting that discount rates are fairly consistent across sales.\n",
        "\n",
        "**Discount Amount:** The mean discount amount is significantly higher than the median, indicating that while most discounts are moderate, some large discounts skew the average higher.\n",
        "\n",
        "**Net Sales Value:** Specific statistics are not displayed, but this typically reflects sales after discounts."
      ],
      "metadata": {
        "id": "6oxua8hcORK9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Visualization"
      ],
      "metadata": {
        "id": "cIv4Y1SYORK9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Histograms:"
      ],
      "metadata": {
        "id": "-hH-jFZ-ORK-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Making plot of data with the column volume, 20 bins and box plot above the histogram\n",
        "fig = px.histogram(data, x='Volume',nbins=20,template='plotly_dark',text_auto=True)\n",
        "fig.update_traces(marker_line_color='yellow',marker_line_width=1.2) #Mark edges of histogram with white color\n",
        "fig.update_layout(title = 'Distribution of volume',  #title of plot\n",
        "                  title_x = 0.5,                     #position of title\n",
        "                  xaxis_title='Volume',              #title for x-axis\n",
        "                  yaxis_title='Frequency')           #title for y-axis\n",
        "fig.show() #Displaying histogram"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-20T17:27:12.798309Z",
          "iopub.execute_input": "2024-07-20T17:27:12.799603Z",
          "iopub.status.idle": "2024-07-20T17:27:12.876886Z",
          "shell.execute_reply.started": "2024-07-20T17:27:12.799566Z",
          "shell.execute_reply": "2024-07-20T17:27:12.875609Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Uy-bRYpWORK-",
        "outputId": "450df099-2e44-40b7-ad06-55bd170baa5c"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
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      "cell_type": "markdown",
      "source": [
        "**From the above histogram plot, I conclude that:**\n",
        "\n",
        "*   The column is log-normally distributed and right-skewed.\n",
        "*   The column contains outliers.\n",
        "*   It contains extreme values, but there are no unusual distributions within the column.\n",
        "*   Initially, the first bin shows low values, which then suddenly increase and gradually decrease in the remaining bins.\n",
        "*   There are no outliers below the lower bound.\n",
        "*   The outliers are present above the upper bound.\n"
      ],
      "metadata": {
        "id": "SERsbyHBORK-"
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    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.histogram(data, x='Avg Price',nbins = 20, text_auto=True,template='plotly_dark')\n",
        "fig.update_traces(marker_line_color='yellow',marker_line_width=1.2)\n",
        "fig.update_layout(title='Distribution of Avg Prices',\n",
        "                  title_x=0.5,\n",
        "                  xaxis_title='Avg Prices',\n",
        "                  yaxis_title='Frequency')\n",
        "fig.show()"
      ],
      "metadata": {
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          "iopub.status.busy": "2024-07-20T17:27:16.184322Z",
          "iopub.execute_input": "2024-07-20T17:27:16.184741Z",
          "iopub.status.idle": "2024-07-20T17:27:16.262784Z",
          "shell.execute_reply.started": "2024-07-20T17:27:16.184707Z",
          "shell.execute_reply": "2024-07-20T17:27:16.261725Z"
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        "trusted": true,
        "id": "BoOphk7_ORK-",
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          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "c3890f97-b6f1-4cf1-c394-3f34b182a44b"
      },
      "execution_count": 38,
      "outputs": [
        {
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        "**Based on the histogram plot, I conclude the following:**\n",
        "\n",
        "*  The distribution of the column follows a Poisson distribution.\n",
        "*  There are outliers at the upper bound.\n",
        "*  Some bins have no values, which may indicate an unusual distribution.\n",
        "*  The first bin contains the most values, while the remaining bins have very few.\n",
        "*  There are no outliers below the lower bound.\n",
        "* Outliers are present above the upper bound.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
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        "fig = px.histogram(data, x='Total Sales Value',nbins=10,text_auto=True,template='plotly_dark')\n",
        "fig.update_traces(marker_line_width=1.2,marker_line_color='yellow')\n",
        "fig.update_layout(title='Distribution of Total Sales Value',\n",
        "                 title_x=0.5,\n",
        "                 xaxis_title='Total Sales Value',\n",
        "                 yaxis_title='Frequency')\n",
        "fig.show()"
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      "cell_type": "markdown",
      "source": [
        "**Based on the histogram plot, I conclude that:**\n",
        "\n",
        "*  This data follows a Poisson distribution.\n",
        "*  It shares similar properties with the previous column, but there is no unusual distribution in this column.\n"
      ],
      "metadata": {
        "id": "gJTaf7olORK_"
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      "cell_type": "code",
      "source": [
        "fig = px.histogram(data, x='Discount Rate (%)',nbins=20, text_auto=True,template='plotly_dark')\n",
        "fig.update_traces(marker_line_width=1.2,marker_line_color='yellow')\n",
        "fig.update_layout(title='Distribution Discount Rate (%)',\n",
        "                 title_x=0.5,\n",
        "                 xaxis_title='Discount Rate (%)',\n",
        "                 yaxis_title='Frequency')\n",
        "fig.show()"
      ],
      "metadata": {
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          "iopub.execute_input": "2024-07-20T17:39:09.277299Z",
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          "shell.execute_reply.started": "2024-07-20T17:39:09.277269Z",
          "shell.execute_reply": "2024-07-20T17:39:09.356510Z"
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        "trusted": true,
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          "height": 542
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        "outputId": "d806bdc0-160d-4656-d8a8-b9f65a3ff1cb"
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      "execution_count": 40,
      "outputs": [
        {
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      "source": [
        "**Based on the histogram plot, I conclude that:**\n",
        "\n",
        "* The column is log-normally distributed and left-skewed.\n",
        "* There are outliers present.\n",
        "* There are no unusual distributions in the column.\n",
        "* Low values are distributed in the initial bins, while higher values are distributed in the remaining bins.\n",
        "* There are no outliers at the upper bound.\n",
        "* Outliers are present below the lower bound.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "wUvlWl-WORLA"
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    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.histogram(data, x='Discount Amount',nbins=15,template='plotly_dark',text_auto=True)\n",
        "fig.update_traces(marker_line_color='yellow',marker_line_width=1.2)\n",
        "fig.update_layout(title='distribution of Discount Amount',\n",
        "                 title_x=0.5,\n",
        "                 xaxis_title='Discount Amount',\n",
        "                 yaxis_title='Frequency')\n",
        "fig.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-20T17:41:57.926521Z",
          "iopub.execute_input": "2024-07-20T17:41:57.926924Z",
          "iopub.status.idle": "2024-07-20T17:41:58.003525Z",
          "shell.execute_reply.started": "2024-07-20T17:41:57.926891Z",
          "shell.execute_reply": "2024-07-20T17:41:58.002227Z"
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        "trusted": true,
        "id": "LeLxLizIORLA",
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          "height": 542
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        "outputId": "9ca4074d-df5b-485d-b2c2-1b2e4bbae806"
      },
      "execution_count": 41,
      "outputs": [
        {
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      "source": [
        "**Based on the above histogram plot, I conclude that:**\n",
        "\n",
        "* The column follows a Poisson distribution.\n",
        "* More values are distributed in the initial bin, then the number of distributed values decreases.\n",
        "* At the upper bound, a small number of values are distributed, which may be outliers.\n",
        "* Outliers are present at the upper bound, but there are no outliers at the lower bound."
      ],
      "metadata": {
        "id": "mnEsCUriORLA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.histogram(data, x='Net Sales Value',nbins=20,text_auto=True,template='plotly_dark')\n",
        "fig.update_traces(marker_line_width=1.2,marker_line_color='yellow')\n",
        "fig.update_layout(title='Net Sales Value',\n",
        "                 title_x=0.5,\n",
        "                 xaxis_title='Net Sales Value',\n",
        "                 yaxis_title='Frequency')\n",
        "fig.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-20T17:51:47.809931Z",
          "iopub.execute_input": "2024-07-20T17:51:47.810594Z",
          "iopub.status.idle": "2024-07-20T17:51:47.886735Z",
          "shell.execute_reply.started": "2024-07-20T17:51:47.810564Z",
          "shell.execute_reply": "2024-07-20T17:51:47.885580Z"
        },
        "trusted": true,
        "id": "B-rXas5aORLA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "9a8422fe-3bbc-44c8-c7f8-62b482c514e0"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Based on the above histogram, I conclude that:**\n",
        "\n",
        "* The column follows a Poisson distribution.\n",
        "* There is no unusual distribution in the column."
      ],
      "metadata": {
        "id": "kXJGAp21ORLB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Boxplots:"
      ],
      "metadata": {
        "id": "kUaOiEeSORLB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.box(data, x='Volume',template='plotly_dark')\n",
        "fig.update_layout(title='Box Plot for Volume',\n",
        "                 title_x=0.5)\n",
        "fig.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-20T18:06:34.339529Z",
          "iopub.execute_input": "2024-07-20T18:06:34.340344Z",
          "iopub.status.idle": "2024-07-20T18:06:34.414082Z",
          "shell.execute_reply.started": "2024-07-20T18:06:34.340309Z",
          "shell.execute_reply": "2024-07-20T18:06:34.412943Z"
        },
        "trusted": true,
        "id": "yv5jInurORLB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "4193952e-348e-49af-d9b6-ce0ce8c56f70"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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    {
      "cell_type": "markdown",
      "source": [
        "**Based on the above Box Plot, I conclude that:**\n",
        "* There are outliers at the upper bound.\n",
        "* More values are distributed above the median.\n",
        "* There are a total of 12 outlier values in this column.\n",
        "* The upper bound is 10, and the lower bound is 1.\n"
      ],
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        "id": "ro9Cn9meORLB"
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    {
      "cell_type": "code",
      "source": [
        "fig = px.box(data, x='Avg Price',template='plotly_dark')\n",
        "fig.update_layout(title='Box plot for Avg Price',\n",
        "                 title_x=0.5)\n",
        "fig.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-20T18:06:34.543762Z",
          "iopub.execute_input": "2024-07-20T18:06:34.544195Z",
          "iopub.status.idle": "2024-07-20T18:06:34.616018Z",
          "shell.execute_reply.started": "2024-07-20T18:06:34.544164Z",
          "shell.execute_reply": "2024-07-20T18:06:34.614675Z"
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        "trusted": true,
        "id": "e5lfDaqVORLB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "676fbee2-6605-45ad-c597-ef5efd17f0c1"
      },
      "execution_count": 44,
      "outputs": [
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        "**Based on the above Box Plot, I conclude that:**\n",
        "* There are outliers at the upper bound.\n",
        "* The majority of values are distributed above the median.\n",
        "* There are a total of 4 outliers in this column.\n",
        "* The upper bound is 20.1k, and the lower bound is 290.\n",
        "\n",
        "\n",
        "\n"
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      "source": [
        "fig = px.box(data, x='Total Sales Value',template='plotly_dark')\n",
        "fig.update_layout(title='Box Plot for Total Sales Value',\n",
        "                 title_x=0.5)\n",
        "fig.show()"
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      "metadata": {
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      "execution_count": 45,
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      "source": [
        "**Based on the above Box Plot, I conclude that:**\n",
        "* There are outliers at the upper bound.\n",
        "* The majority of values are distributed above the median.\n",
        "* There are a total of 11 outliers in this column.\n",
        "* The upper bound is 120.6k, and the lower bound is 400."
      ],
      "metadata": {
        "id": "OkFwY-d2ORLC"
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    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.box(data, x='Discount Rate (%)',template='plotly_dark')\n",
        "fig.update_layout(title='Box Plot for Discount Rate (%)',\n",
        "                 title_x=0.5)\n",
        "fig.show()"
      ],
      "metadata": {
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          "iopub.status.busy": "2024-07-20T18:06:34.981831Z",
          "iopub.execute_input": "2024-07-20T18:06:34.982279Z",
          "iopub.status.idle": "2024-07-20T18:06:35.058077Z",
          "shell.execute_reply.started": "2024-07-20T18:06:34.982245Z",
          "shell.execute_reply": "2024-07-20T18:06:35.056829Z"
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        "trusted": true,
        "id": "-PufIQLeORLD",
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          "height": 542
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        "outputId": "08a9763e-5c19-4d7e-88b7-774fef38af78"
      },
      "execution_count": 46,
      "outputs": [
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        "**Based on the above Box Plot, I conclude that:**\n",
        "* There are outliers at the lower bound.\n",
        "* The majority of values are distributed above the median.\n",
        "* There are several outliers in this column.\n",
        "* The upper bound is 19.99, and the lower bound is 5.00."
      ],
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      "cell_type": "code",
      "source": [
        "fig = px.box(data, x='Discount Amount',template='plotly_dark')\n",
        "fig.update_layout(title='Box Plot for Discount Amount',\n",
        "                 title_x=0.5)\n",
        "fig.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-20T18:06:39.795928Z",
          "iopub.execute_input": "2024-07-20T18:06:39.796401Z",
          "iopub.status.idle": "2024-07-20T18:06:39.872393Z",
          "shell.execute_reply.started": "2024-07-20T18:06:39.796358Z",
          "shell.execute_reply": "2024-07-20T18:06:39.871121Z"
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          "height": 542
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        "outputId": "bba3f12c-b63d-446b-e5fb-b84ebf25e3dc"
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      "execution_count": 47,
      "outputs": [
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    {
      "cell_type": "markdown",
      "source": [
        "**Based on the above Box Plot, I conclude that:**\n",
        "* There are outliers at the upper bound.\n",
        "* The majority of values are distributed above the median.\n",
        "* The upper bound is 25.73k, and the lower bound is 69.17."
      ],
      "metadata": {
        "id": "jeXP_x79ORLD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.box(data, x='Net Sales Value',template='plotly_dark')\n",
        "fig.update_layout(title='Net Sales Value',\n",
        "                 title_x=0.5,\n",
        "                 xaxis_title='Net Sales Value')\n",
        "fig.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-20T18:06:40.763396Z",
          "iopub.execute_input": "2024-07-20T18:06:40.763849Z",
          "iopub.status.idle": "2024-07-20T18:06:40.837764Z",
          "shell.execute_reply.started": "2024-07-20T18:06:40.763816Z",
          "shell.execute_reply": "2024-07-20T18:06:40.836578Z"
        },
        "trusted": true,
        "id": "C0J7NUKQORLD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "67e22f2b-7100-4d42-b55e-25c0ef0256df"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "markdown",
      "source": [
        "**Based on the above Box Plot, I conclude that:**\n",
        "* There are outliers at the upper bound.\n",
        "* The majority of values are distributed above the median.\n",
        "* The upper bound is 179.50k, and the lower bound is 326.97."
      ],
      "metadata": {
        "id": "6avqczlyORLE"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# Bar Chart Analysis for Categorical Column:"
      ],
      "metadata": {
        "id": "p29bdDqKORLE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Categorical_Columns = list(set(data.columns)-set(data.describe().columns)) #Get the Categorical columns in the data\n",
        "Categorical_Columns #Displaying the categorical columns"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-15T15:32:19.853313Z",
          "iopub.execute_input": "2024-07-15T15:32:19.853651Z",
          "iopub.status.idle": "2024-07-15T15:32:19.874208Z",
          "shell.execute_reply.started": "2024-07-15T15:32:19.853622Z",
          "shell.execute_reply": "2024-07-15T15:32:19.872767Z"
        },
        "trusted": true,
        "id": "6zT29MBKORLK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "008e79e6-1a3a-4bfd-94fd-fae7b256ec5d"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['SKU', 'Brand', 'BU', 'Day', 'City', 'Model', 'Date']"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = data['City'].value_counts().reset_index() #Getting unique values in the column with respective their count\n",
        "df1 = pd.DataFrame(df1) #creating dataframe for unique values in the column with respective their count\n",
        "df1"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-20T18:06:43.390895Z",
          "iopub.execute_input": "2024-07-20T18:06:43.392020Z",
          "iopub.status.idle": "2024-07-20T18:06:43.405728Z",
          "shell.execute_reply.started": "2024-07-20T18:06:43.391984Z",
          "shell.execute_reply": "2024-07-20T18:06:43.404367Z"
        },
        "trusted": true,
        "id": "-PQ73l8iORLL",
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          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "73c390b3-f5a8-4f18-fb2c-c01ead53e08f"
      },
      "execution_count": 50,
      "outputs": [
        {
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            "text/plain": [
              "  City  count\n",
              "0    C    450"
            ],
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        "fig = px.bar(df1,x='City',y='count',text_auto=True,template='plotly_dark',color='City') #Creating a plot for categorical column with respective their count\n",
        "fig.update_traces(textposition='outside') #Put text position outside of the bar\n",
        "fig.update_layout(title='Bar Chart For City', #title of the plot\n",
        "                 title_x=0.5) #title position in the plot\n",
        "fig.show() #displaying the plot"
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      "source": [
        "**Based on the above bar plot, I conclude that:**\n",
        "* There is only one unique value in this column.\n",
        "* The count of this unique value is 450."
      ],
      "metadata": {
        "id": "WUoWc85_ORLL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = data['Date'].value_counts().reset_index() #Getting unique values in the column with respective their count\n",
        "df1 = pd.DataFrame(df1) #creating dataframe for unique values in the column with respective their count\n",
        "fig = px.bar(df1,x='Date',y='count',text_auto=True,template='plotly_dark',color='Date') #Creating a plot for categorical column with respective their count\n",
        "fig.update_traces(textposition='outside') #Put text position outside of the bar\n",
        "fig.update_layout(title='Bar Chart For Date', #title of the plot\n",
        "                 title_x=0.5) #title position in the plot\n",
        "fig.show() #displaying the plot"
      ],
      "metadata": {
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      "execution_count": 52,
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      "source": [
        "**Based on the above bar plot, I conclude that:**\n",
        "\n",
        "* All categories in this column are equally distributed.\n",
        "* The count for each unique value is 30."
      ],
      "metadata": {
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      "source": [
        "df1 = data['Day'].value_counts().reset_index() #Getting unique values in the column with respective their count\n",
        "df1 = pd.DataFrame(df1) #creating dataframe for unique values in the column with respective their count\n",
        "fig = px.bar(df1,x='Day',y='count',text_auto=True,template='plotly_dark',color='Day') #Creating a plot for categorical column with respective their count\n",
        "fig.update_traces(textposition='outside') #Put text position outside of the bar\n",
        "fig.update_layout(title='Bar Chart For Day', #title of the plot\n",
        "                 title_x=0.5) #title position in the plot\n",
        "fig.show() #displaying the plot"
      ],
      "metadata": {
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      "execution_count": 53,
      "outputs": [
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    {
      "cell_type": "markdown",
      "source": [
        "**Based on the above bar plot, I conclude that:**\n",
        "* There are a total of 7 unique values in this column, representing the days of the week.\n",
        "* All values are distributed equally, except for Thursday, which has a different count.\n",
        "* The highest number of sales occur on Thursday."
      ],
      "metadata": {
        "id": "R3jjfXdFORLM"
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    {
      "cell_type": "code",
      "source": [
        "df1 = data['SKU'].value_counts().reset_index() #Getting unique values in the column with respective their count\n",
        "df1 = pd.DataFrame(df1) #creating dataframe for unique values in the column with respective their count\n",
        "fig = px.bar(df1,x='SKU',y='count',text_auto=True,template='plotly_dark',color='SKU') #Creating a plot for categorical column with respective their count\n",
        "fig.update_traces(textposition='outside') #Put text position outside of the bar\n",
        "fig.update_layout(title='Bar Chart For SKU', #title of the plot\n",
        "                 title_x=0.5) #title position in the plot\n",
        "fig.show() #displaying the plot"
      ],
      "metadata": {
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      "execution_count": 54,
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    {
      "cell_type": "markdown",
      "source": [
        "**Based on the above bar plot, I conclude that:**\n",
        "* All categories in this column are equally distributed.\n",
        "* The count for each unique value is 15."
      ],
      "metadata": {
        "id": "rqWwT2Y6ORLN"
      }
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    {
      "cell_type": "code",
      "source": [
        "df1 = data['Model'].value_counts().reset_index() #Getting unique values in the column with respective their count\n",
        "df1 = pd.DataFrame(df1) #creating dataframe for unique values in the column with respective their count\n",
        "fig = px.bar(df1,x='Model',y='count',text_auto=True,template='plotly_dark',color='Model') #Creating a plot for categorical column with respective their count\n",
        "fig.update_traces(textposition='outside') #Put text position outside of the bar\n",
        "fig.update_layout(title='Bar Chart For Model', #title of the plot\n",
        "                 title_x=0.5) #title position in the plot\n",
        "fig.show() #displaying the plot"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-15T15:32:20.348025Z",
          "iopub.execute_input": "2024-07-15T15:32:20.348319Z",
          "iopub.status.idle": "2024-07-15T15:32:20.519992Z",
          "shell.execute_reply.started": "2024-07-15T15:32:20.348291Z",
          "shell.execute_reply": "2024-07-15T15:32:20.518603Z"
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        "id": "noU6jKPlORLN",
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          "height": 542
        },
        "outputId": "67975fd3-0ffc-4e67-f6c4-72b0bb3adea4"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
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    {
      "cell_type": "markdown",
      "source": [
        "**Based on the above bar plot, I conclude that:**\n",
        "* All categories in this column are equally distributed.\n",
        "* The count for each unique value is 15."
      ],
      "metadata": {
        "id": "o5COpt5PORLN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = data['BU'].value_counts().reset_index() #Getting unique values in the column with respective their count\n",
        "df1 = pd.DataFrame(df1) #creating dataframe for unique values in the column with respective their count\n",
        "fig = px.bar(df1,x='BU',y='count',text_auto=True,template='plotly_dark',color='BU') #Creating a plot for categorical column with respective their count\n",
        "fig.update_traces(textposition='outside') #Put text position outside of the bar\n",
        "fig.update_layout(title='Bar Chart For BU', #title of the plot\n",
        "                 title_x=0.5) #title position in the plot\n",
        "fig.show() #displaying the plot"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-15T15:32:20.521232Z",
          "iopub.execute_input": "2024-07-15T15:32:20.521609Z",
          "iopub.status.idle": "2024-07-15T15:32:20.587253Z",
          "shell.execute_reply.started": "2024-07-15T15:32:20.521576Z",
          "shell.execute_reply": "2024-07-15T15:32:20.586212Z"
        },
        "trusted": true,
        "id": "j2cffVFwORLN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "7c159442-eb7a-496f-e266-55df6fd3b9a6"
      },
      "execution_count": 56,
      "outputs": [
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      "source": [
        "**Based on the above bar plot, I conclude that:**\n",
        "\n",
        "* All categories in this column are equally distributed.\n",
        "* The count for each unique value is 150.\n",
        "* There are a total of 3 categories."
      ],
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      "source": [
        "df1 = data['Brand'].value_counts().reset_index() #Getting unique values in the column with respective their count\n",
        "df1 = pd.DataFrame(df1) #creating dataframe for unique values in the column with respective their count\n",
        "fig = px.bar(df1,x='Brand',y='count',text_auto=True,template='plotly_dark',color='Brand') #Creating a plot for categorical column with respective their count\n",
        "fig.update_traces(textposition='outside') #Put text position outside of the bar\n",
        "fig.update_layout(title='Bar Chart For Brand', #title of the plot\n",
        "                 title_x=0.5) #title position in the plot\n",
        "fig.show() #displaying the plot"
      ],
      "metadata": {
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      "execution_count": 57,
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Based on the above bar plot, I conclude that:**\n",
        "\n",
        "* The categories in this column are unequally distributed.\n",
        "* The brand Jeera has the highest number of sales."
      ],
      "metadata": {
        "id": "6lqxWXTFORLO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Standardization of Numerical Variables"
      ],
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        "id": "x0pWUWOYORLO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "* Standardization involves transforming raw data values to a standard scale with a mean of 0 and a standard deviation of 1.\n",
        "*  This is achieved using the formula:\n",
        "z=x-mu/sigma.\n",
        "* Standardization helps reduce the effect of extreme values, improves model performance, and makes data more comparable across different datasets.\n",
        "\n",
        "* It is commonly used in machine learning, finance, and scientific research to preprocess data before analysis or modeling."
      ],
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        "id": "Fmi1UykyORLO"
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      "source": [
        "df #Before making z normalization"
      ],
      "metadata": {
        "execution": {
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          "iopub.execute_input": "2024-07-15T15:32:20.680505Z",
          "iopub.status.idle": "2024-07-15T15:32:20.698671Z",
          "shell.execute_reply.started": "2024-07-15T15:32:20.680473Z",
          "shell.execute_reply": "2024-07-15T15:32:20.697504Z"
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        "trusted": true,
        "id": "eVeMNCT9ORLP",
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          "height": 237
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        "outputId": "309ad987-b510-4ddb-bf9e-1808156006e5"
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      "execution_count": 58,
      "outputs": [
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            "text/plain": [
              "                           Mean       Median  Standard Deviation  \\\n",
              "Volume                 5.066667     4.000000            4.231602   \n",
              "Avg Price          10453.433333  1450.000000        18079.904840   \n",
              "Total Sales Value  33812.835556  5700.000000        50535.074173   \n",
              "Discount Rate (%)     15.155242    16.577766            4.220602   \n",
              "Discount Amount     3346.499424   988.933733         4509.902963   \n",
              "Net Sales Value    30466.336131  4677.788059        46358.656624   \n",
              "\n",
              "                                          Mode  \n",
              "Volume                                       3  \n",
              "Avg Price          [400, 450, 500, 1300, 8100]  \n",
              "Total Sales Value                        24300  \n",
              "Discount Rate (%)                        17.51  \n",
              "Discount Amount                         735.71  \n",
              "Net Sales Value                           None  "
            ],
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              "      <th></th>\n",
              "      <th>Mean</th>\n",
              "      <th>Median</th>\n",
              "      <th>Standard Deviation</th>\n",
              "      <th>Mode</th>\n",
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              "      <th>Volume</th>\n",
              "      <td>5.066667</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.231602</td>\n",
              "      <td>3</td>\n",
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              "      <th>Avg Price</th>\n",
              "      <td>10453.433333</td>\n",
              "      <td>1450.000000</td>\n",
              "      <td>18079.904840</td>\n",
              "      <td>[400, 450, 500, 1300, 8100]</td>\n",
              "    </tr>\n",
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              "      <th>Total Sales Value</th>\n",
              "      <td>33812.835556</td>\n",
              "      <td>5700.000000</td>\n",
              "      <td>50535.074173</td>\n",
              "      <td>24300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount Rate (%)</th>\n",
              "      <td>15.155242</td>\n",
              "      <td>16.577766</td>\n",
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              "      <td>17.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount Amount</th>\n",
              "      <td>3346.499424</td>\n",
              "      <td>988.933733</td>\n",
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              "      <td>735.71</td>\n",
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              "      <th>Net Sales Value</th>\n",
              "      <td>30466.336131</td>\n",
              "      <td>4677.788059</td>\n",
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              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .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",
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              "    }\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1bfaf8d7-a918-4da5-9319-58d247398f4b 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-1bfaf8d7-a918-4da5-9319-58d247398f4b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "    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",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      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-e70a4e99-4f1a-4eee-a19a-4ffdf9e7c625 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_43ed3c03-6341-4166-aabe-a0021215edae\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15332.28579375928,\n        \"min\": 5.066666666666666,\n        \"max\": 33812.83555555555,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          5.066666666666666,\n          10453.433333333332,\n          30466.336131270138\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Median\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2448.936036500305,\n        \"min\": 4.0,\n        \"max\": 5700.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.0,\n          1450.0,\n          4677.788059301853\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Standard Deviation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23110.250282900754,\n        \"min\": 4.22060215332551,\n        \"max\": 50535.07417255325,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4.231602391213923,\n          18079.90483993647,\n          46358.65662406695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mode\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().columns #Get numerical columns"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-15T15:32:20.700135Z",
          "iopub.execute_input": "2024-07-15T15:32:20.700547Z",
          "iopub.status.idle": "2024-07-15T15:32:20.720890Z",
          "shell.execute_reply.started": "2024-07-15T15:32:20.700517Z",
          "shell.execute_reply": "2024-07-15T15:32:20.719706Z"
        },
        "trusted": true,
        "id": "3sYxc7DNORLP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c24e5fe-8d2f-40b1-a567-86eb24656380"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Mean', 'Median', 'Standard Deviation'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for x in df.describe().columns:\n",
        "    l = [] #Initilizing the list to append all values to this list later update values using this list\n",
        "    for y in df[x]:\n",
        "        mean = df[x].mean() #Calculating the mean of the column\n",
        "        std = df[x].std() #Calculating the standard deviation of the column\n",
        "        z = (y-mean)/std #calculating the z value\n",
        "        l.append(round(z,2)) #append calculated z values to list\n",
        "    df[x].update(l) #Updating the column with list\n",
        "df #Displaying the column after normalization"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-15T15:32:20.722047Z",
          "iopub.execute_input": "2024-07-15T15:32:20.723008Z",
          "iopub.status.idle": "2024-07-15T15:32:21.010087Z",
          "shell.execute_reply.started": "2024-07-15T15:32:20.722977Z",
          "shell.execute_reply": "2024-07-15T15:32:21.009057Z"
        },
        "trusted": true,
        "id": "J1uZjSKnORLP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "fdea3a3c-bc15-4a58-e855-2aea468d1647"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           Mean       Median  Standard Deviation  \\\n",
              "Volume                 5.066667     4.000000            4.231602   \n",
              "Avg Price          10453.433333  1450.000000        18079.904840   \n",
              "Total Sales Value  33812.835556  5700.000000        50535.074173   \n",
              "Discount Rate (%)     15.155242    16.577766            4.220602   \n",
              "Discount Amount     3346.499424   988.933733         4509.902963   \n",
              "Net Sales Value    30466.336131  4677.788059        46358.656624   \n",
              "\n",
              "                                          Mode  \n",
              "Volume                                       3  \n",
              "Avg Price          [400, 450, 500, 1300, 8100]  \n",
              "Total Sales Value                        24300  \n",
              "Discount Rate (%)                        17.51  \n",
              "Discount Amount                         735.71  \n",
              "Net Sales Value                           None  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b8b0f110-9c95-4e7a-809e-325551f28688\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Mean</th>\n",
              "      <th>Median</th>\n",
              "      <th>Standard Deviation</th>\n",
              "      <th>Mode</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Volume</th>\n",
              "      <td>5.066667</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.231602</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Avg Price</th>\n",
              "      <td>10453.433333</td>\n",
              "      <td>1450.000000</td>\n",
              "      <td>18079.904840</td>\n",
              "      <td>[400, 450, 500, 1300, 8100]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Total Sales Value</th>\n",
              "      <td>33812.835556</td>\n",
              "      <td>5700.000000</td>\n",
              "      <td>50535.074173</td>\n",
              "      <td>24300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount Rate (%)</th>\n",
              "      <td>15.155242</td>\n",
              "      <td>16.577766</td>\n",
              "      <td>4.220602</td>\n",
              "      <td>17.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Discount Amount</th>\n",
              "      <td>3346.499424</td>\n",
              "      <td>988.933733</td>\n",
              "      <td>4509.902963</td>\n",
              "      <td>735.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Net Sales Value</th>\n",
              "      <td>30466.336131</td>\n",
              "      <td>4677.788059</td>\n",
              "      <td>46358.656624</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b8b0f110-9c95-4e7a-809e-325551f28688');\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",
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            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conversion of Categorical Data into Dummy Variables"
      ],
      "metadata": {
        "id": "vRqIvm1AORLP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* One-hot encoding involves converting categorical data into dummy variables.\n",
        "* This transformation is necessary because algorithms such as linear regression can only handle continuous or numerical data.\n",
        "* Categorical data cannot be directly processed by these models.\n",
        "* One-hot encoding transforms categorical data into a binary format, where each category is represented as a separate binary column.\n",
        "* This allows algorithms to differentiate between distinct categories and enables accurate modeling and prediction of categorical relationships, thereby improving the overall performance of machine learning models.  \n",
        "\n"
      ],
      "metadata": {
        "id": "ygaXa5AKORLQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d = pd.get_dummies(data,columns=Categorical_Columns,dtype=int)\n",
        "d.head()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-07-15T16:04:33.161631Z",
          "iopub.execute_input": "2024-07-15T16:04:33.161949Z",
          "iopub.status.idle": "2024-07-15T16:04:33.189704Z",
          "shell.execute_reply.started": "2024-07-15T16:04:33.161926Z",
          "shell.execute_reply": "2024-07-15T16:04:33.188728Z"
        },
        "trusted": true,
        "id": "pZTFKHklORLQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "27a64d69-a327-4a82-88f9-cd83f11fbc00"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   Volume  Avg Price  Total Sales Value  Discount Rate (%)  Discount Amount  \\\n",
              "0      15      12100             181500          11.654820     21153.498820   \n",
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              "\n",
              "   Net Sales Value  SKU_F01  SKU_F02  SKU_F03  SKU_F04  ...  Date_06-04-2021  \\\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b1cc00b5-59ea-4704-8e14-3bcd771ca3a4 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-b1cc00b5-59ea-4704-8e14-3bcd771ca3a4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2fa67224-4d85-483d-8344-4e86254c15c9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2fa67224-4d85-483d-8344-4e86254c15c9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2fa67224-4d85-483d-8344-4e86254c15c9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "d"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusion"
      ],
      "metadata": {
        "id": "ZVGb04y7ORLQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Data preprocessing is a crucial step in data analysis and machine learning, as it enables accurate and reliable modeling.\n",
        "* Standardization and one-hot encoding are two essential preprocessing techniques.\n",
        "* Standardization scales data to a common range to prevent features with large ranges from dominating others.\n",
        "* One-hot encoding converts categorical variables into binary vectors, allowing them to be processed by models.\n",
        "* These techniques help prevent data leakage, improve model interpretability, and enable more efficient computation.\n",
        "* By applying standardization and one-hot encoding, you can ensure that your data is clean, consistent, and ready for analysis and modeling.  "
      ],
      "metadata": {
        "id": "tdbTsAWLORLR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "1. **Sales Volume Distribution**\n",
        "\n",
        "    * **Mean Volume:** 5.07 units\n",
        "    * **Standard Deviation:** 4.23 units\n",
        "    * **Range:** 1 to 450 units.\n",
        "\n",
        "The histogram shows a high frequency at the lower end, indicating that most sales volumes are low.\n",
        "2. **Average Price Distribution**\n",
        "\n",
        "* **Mean Average Price:** 10,453.40\n",
        "* **Standard Deviation:** 18,079.90\n",
        "* **Range:** 290 to 60,100\n",
        "\n",
        "The histogram reveals a right-skewed distribution, suggesting that while most products are priced lower, a few high-priced items significantly raise the average price.\n",
        "\n",
        "3. **Total Sales Value Distribution**\n",
        "\n",
        "* **Mean Total Sales Value:** 33,812.80\n",
        "* **Standard Deviation:** 50,535.10\n",
        "* **Range:** 400 to 196,400\n",
        "\n",
        "The histogram indicates a right-skewed distribution, similar to average prices, with most sales values clustering at the lower end.\n",
        "\n",
        "4. **Discount Rate Distribution**\n",
        "\n",
        "* **Mean Discount Rate:** 15.16%\n",
        "* **Standard Deviation:** 4.22%\n",
        "* **Range:** 5.01% to 450%  \n",
        "\n",
        "The histogram shows a concentration of discount rates at lower percentages, with a few outliers showing extremely high discount rates.\n",
        "\n",
        "5. **Net Sales Value Distribution**\n",
        "\n",
        "* **Mean Net Sales Value:** 30,466.34\n",
        "* **Standard Deviation:** 46,358.66\n",
        "* **Range:** 326.97 to 179,507.48\n",
        "\n",
        "The histogram for net sales values shows a distribution with multiple peaks, indicating variability in net sales across different values.\n",
        "\n"
      ],
      "metadata": {
        "id": "X0sxPrdT_Ml3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Preprocessing:**\n",
        "Data preprocessing is a crucial step in data analysis and machine learning, as it ensures accurate and reliable modeling. Two essential preprocessing techniques are:\n",
        "\n",
        "* **Standardization:** This technique scales data to a common range to prevent features with large ranges from dominating others. It transforms data to have a mean of 0 and a standard deviation of 1.\n",
        "\n",
        "* **One-Hot Encoding:** This technique converts categorical variables into binary vectors, allowing them to be processed by models.\n",
        "\n",
        "These preprocessing techniques help prevent data leakage, improve model interpretability, and enable more efficient computation. By applying standardization and one-hot encoding, you ensure that your data is clean, consistent, and ready for analysis and modeling."
      ],
      "metadata": {
        "id": "zmn0nMJXB-_g"
      }
    }
  ]
}