Supply_Chain_Analysis.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Import Libraries**"
      ],
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        "id": "mLcBBwjsmT7X"
      }
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    {
      "cell_type": "code",
      "execution_count": 2,
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      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import plotly.io as pio\n",
        "import plotly.graph_objects as go\n",
        "pio.templates.default = \"plotly_white\""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Read Data**"
      ],
      "metadata": {
        "id": "f5a3JuyCmJXt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_rows',None)\n",
        "pd.set_option('display.max_columns',None)\n"
      ],
      "metadata": {
        "id": "0EzKlY2CgJ62"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv(\"/content/supply_chain_data.csv\")\n",
        "data.head()"
      ],
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          "height": 365
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        "id": "-dgLbnx9lSdb",
        "outputId": "7a58b363-bd53-417f-c443-6d8185f1dce4"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Product type   SKU      Price  Availability  Number of products sold  \\\n",
              "0     haircare  SKU0  69.808006            55                      802   \n",
              "1     skincare  SKU1  14.843523            95                      736   \n",
              "2     haircare  SKU2  11.319683            34                        8   \n",
              "3     skincare  SKU3  61.163343            68                       83   \n",
              "4     skincare  SKU4   4.805496            26                      871   \n",
              "\n",
              "   Revenue generated Customer demographics  Stock levels  Lead times  \\\n",
              "0        8661.996792            Non-binary            58           7   \n",
              "1        7460.900065                Female            53          30   \n",
              "2        9577.749626               Unknown             1          10   \n",
              "3        7766.836426            Non-binary            23          13   \n",
              "4        2686.505152            Non-binary             5           3   \n",
              "\n",
              "   Order quantities  Shipping times Shipping carriers  Shipping costs  \\\n",
              "0                96               4         Carrier B        2.956572   \n",
              "1                37               2         Carrier A        9.716575   \n",
              "2                88               2         Carrier B        8.054479   \n",
              "3                59               6         Carrier C        1.729569   \n",
              "4                56               8         Carrier A        3.890548   \n",
              "\n",
              "  Supplier name Location  Lead time  Production volumes  \\\n",
              "0    Supplier 3   Mumbai         29                 215   \n",
              "1    Supplier 3   Mumbai         23                 517   \n",
              "2    Supplier 1   Mumbai         12                 971   \n",
              "3    Supplier 5  Kolkata         24                 937   \n",
              "4    Supplier 1    Delhi          5                 414   \n",
              "\n",
              "   Manufacturing lead time  Manufacturing costs Inspection results  \\\n",
              "0                       29            46.279879            Pending   \n",
              "1                       30            33.616769            Pending   \n",
              "2                       27            30.688019            Pending   \n",
              "3                       18            35.624741               Fail   \n",
              "4                        3            92.065161               Fail   \n",
              "\n",
              "   Defect rates Transportation modes   Routes       Costs  \n",
              "0      0.226410                 Road  Route B  187.752075  \n",
              "1      4.854068                 Road  Route B  503.065579  \n",
              "2      4.580593                  Air  Route C  141.920282  \n",
              "3      4.746649                 Rail  Route A  254.776159  \n",
              "4      3.145580                  Air  Route A  923.440632  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Product type</th>\n",
              "      <th>SKU</th>\n",
              "      <th>Price</th>\n",
              "      <th>Availability</th>\n",
              "      <th>Number of products sold</th>\n",
              "      <th>Revenue generated</th>\n",
              "      <th>Customer demographics</th>\n",
              "      <th>Stock levels</th>\n",
              "      <th>Lead times</th>\n",
              "      <th>Order quantities</th>\n",
              "      <th>Shipping times</th>\n",
              "      <th>Shipping carriers</th>\n",
              "      <th>Shipping costs</th>\n",
              "      <th>Supplier name</th>\n",
              "      <th>Location</th>\n",
              "      <th>Lead time</th>\n",
              "      <th>Production volumes</th>\n",
              "      <th>Manufacturing lead time</th>\n",
              "      <th>Manufacturing costs</th>\n",
              "      <th>Inspection results</th>\n",
              "      <th>Defect rates</th>\n",
              "      <th>Transportation modes</th>\n",
              "      <th>Routes</th>\n",
              "      <th>Costs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>haircare</td>\n",
              "      <td>SKU0</td>\n",
              "      <td>69.808006</td>\n",
              "      <td>55</td>\n",
              "      <td>802</td>\n",
              "      <td>8661.996792</td>\n",
              "      <td>Non-binary</td>\n",
              "      <td>58</td>\n",
              "      <td>7</td>\n",
              "      <td>96</td>\n",
              "      <td>4</td>\n",
              "      <td>Carrier B</td>\n",
              "      <td>2.956572</td>\n",
              "      <td>Supplier 3</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>29</td>\n",
              "      <td>215</td>\n",
              "      <td>29</td>\n",
              "      <td>46.279879</td>\n",
              "      <td>Pending</td>\n",
              "      <td>0.226410</td>\n",
              "      <td>Road</td>\n",
              "      <td>Route B</td>\n",
              "      <td>187.752075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>skincare</td>\n",
              "      <td>SKU1</td>\n",
              "      <td>14.843523</td>\n",
              "      <td>95</td>\n",
              "      <td>736</td>\n",
              "      <td>7460.900065</td>\n",
              "      <td>Female</td>\n",
              "      <td>53</td>\n",
              "      <td>30</td>\n",
              "      <td>37</td>\n",
              "      <td>2</td>\n",
              "      <td>Carrier A</td>\n",
              "      <td>9.716575</td>\n",
              "      <td>Supplier 3</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>23</td>\n",
              "      <td>517</td>\n",
              "      <td>30</td>\n",
              "      <td>33.616769</td>\n",
              "      <td>Pending</td>\n",
              "      <td>4.854068</td>\n",
              "      <td>Road</td>\n",
              "      <td>Route B</td>\n",
              "      <td>503.065579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>haircare</td>\n",
              "      <td>SKU2</td>\n",
              "      <td>11.319683</td>\n",
              "      <td>34</td>\n",
              "      <td>8</td>\n",
              "      <td>9577.749626</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>88</td>\n",
              "      <td>2</td>\n",
              "      <td>Carrier B</td>\n",
              "      <td>8.054479</td>\n",
              "      <td>Supplier 1</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>12</td>\n",
              "      <td>971</td>\n",
              "      <td>27</td>\n",
              "      <td>30.688019</td>\n",
              "      <td>Pending</td>\n",
              "      <td>4.580593</td>\n",
              "      <td>Air</td>\n",
              "      <td>Route C</td>\n",
              "      <td>141.920282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>skincare</td>\n",
              "      <td>SKU3</td>\n",
              "      <td>61.163343</td>\n",
              "      <td>68</td>\n",
              "      <td>83</td>\n",
              "      <td>7766.836426</td>\n",
              "      <td>Non-binary</td>\n",
              "      <td>23</td>\n",
              "      <td>13</td>\n",
              "      <td>59</td>\n",
              "      <td>6</td>\n",
              "      <td>Carrier C</td>\n",
              "      <td>1.729569</td>\n",
              "      <td>Supplier 5</td>\n",
              "      <td>Kolkata</td>\n",
              "      <td>24</td>\n",
              "      <td>937</td>\n",
              "      <td>18</td>\n",
              "      <td>35.624741</td>\n",
              "      <td>Fail</td>\n",
              "      <td>4.746649</td>\n",
              "      <td>Rail</td>\n",
              "      <td>Route A</td>\n",
              "      <td>254.776159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>skincare</td>\n",
              "      <td>SKU4</td>\n",
              "      <td>4.805496</td>\n",
              "      <td>26</td>\n",
              "      <td>871</td>\n",
              "      <td>2686.505152</td>\n",
              "      <td>Non-binary</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>56</td>\n",
              "      <td>8</td>\n",
              "      <td>Carrier A</td>\n",
              "      <td>3.890548</td>\n",
              "      <td>Supplier 1</td>\n",
              "      <td>Delhi</td>\n",
              "      <td>5</td>\n",
              "      <td>414</td>\n",
              "      <td>3</td>\n",
              "      <td>92.065161</td>\n",
              "      <td>Fail</td>\n",
              "      <td>3.145580</td>\n",
              "      <td>Air</td>\n",
              "      <td>Route A</td>\n",
              "      <td>923.440632</td>\n",
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          "metadata": {},
          "execution_count": 7
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      "source": [
        "data.describe()"
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        "id": "elHBF9qEl3q8",
        "outputId": "030ff95e-e903-4b73-c8eb-4222be5d9ab0"
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      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Price  Availability  Number of products sold  Revenue generated  \\\n",
              "count  100.000000    100.000000               100.000000         100.000000   \n",
              "mean    49.462461     48.400000               460.990000        5776.048187   \n",
              "std     31.168193     30.743317               303.780074        2732.841744   \n",
              "min      1.699976      1.000000                 8.000000        1061.618523   \n",
              "25%     19.597823     22.750000               184.250000        2812.847151   \n",
              "50%     51.239831     43.500000               392.500000        6006.352023   \n",
              "75%     77.198228     75.000000               704.250000        8253.976921   \n",
              "max     99.171329    100.000000               996.000000        9866.465458   \n",
              "\n",
              "       Stock levels  Lead times  Order quantities  Shipping times  \\\n",
              "count    100.000000  100.000000        100.000000      100.000000   \n",
              "mean      47.770000   15.960000         49.220000        5.750000   \n",
              "std       31.369372    8.785801         26.784429        2.724283   \n",
              "min        0.000000    1.000000          1.000000        1.000000   \n",
              "25%       16.750000    8.000000         26.000000        3.750000   \n",
              "50%       47.500000   17.000000         52.000000        6.000000   \n",
              "75%       73.000000   24.000000         71.250000        8.000000   \n",
              "max      100.000000   30.000000         96.000000       10.000000   \n",
              "\n",
              "       Shipping costs   Lead time  Production volumes  \\\n",
              "count      100.000000  100.000000          100.000000   \n",
              "mean         5.548149   17.080000          567.840000   \n",
              "std          2.651376    8.846251          263.046861   \n",
              "min          1.013487    1.000000          104.000000   \n",
              "25%          3.540248   10.000000          352.000000   \n",
              "50%          5.320534   18.000000          568.500000   \n",
              "75%          7.601695   25.000000          797.000000   \n",
              "max          9.929816   30.000000          985.000000   \n",
              "\n",
              "       Manufacturing lead time  Manufacturing costs  Defect rates       Costs  \n",
              "count                100.00000           100.000000    100.000000  100.000000  \n",
              "mean                  14.77000            47.266693      2.277158  529.245782  \n",
              "std                    8.91243            28.982841      1.461366  258.301696  \n",
              "min                    1.00000             1.085069      0.018608  103.916248  \n",
              "25%                    7.00000            22.983299      1.009650  318.778455  \n",
              "50%                   14.00000            45.905622      2.141863  520.430444  \n",
              "75%                   23.00000            68.621026      3.563995  763.078231  \n",
              "max                   30.00000            99.466109      4.939255  997.413450  "
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              "      <th>count</th>\n",
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              "      <th>mean</th>\n",
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              "      <td>48.400000</td>\n",
              "      <td>460.990000</td>\n",
              "      <td>5776.048187</td>\n",
              "      <td>47.770000</td>\n",
              "      <td>15.960000</td>\n",
              "      <td>49.220000</td>\n",
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              "      <td>5.548149</td>\n",
              "      <td>17.080000</td>\n",
              "      <td>567.840000</td>\n",
              "      <td>14.77000</td>\n",
              "      <td>47.266693</td>\n",
              "      <td>2.277158</td>\n",
              "      <td>529.245782</td>\n",
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              "      <th>std</th>\n",
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              "      <td>30.743317</td>\n",
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              "      <td>2732.841744</td>\n",
              "      <td>31.369372</td>\n",
              "      <td>8.785801</td>\n",
              "      <td>26.784429</td>\n",
              "      <td>2.724283</td>\n",
              "      <td>2.651376</td>\n",
              "      <td>8.846251</td>\n",
              "      <td>263.046861</td>\n",
              "      <td>8.91243</td>\n",
              "      <td>28.982841</td>\n",
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              "      <td>258.301696</td>\n",
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              "      <td>8.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.013487</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>104.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.085069</td>\n",
              "      <td>0.018608</td>\n",
              "      <td>103.916248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>19.597823</td>\n",
              "      <td>22.750000</td>\n",
              "      <td>184.250000</td>\n",
              "      <td>2812.847151</td>\n",
              "      <td>16.750000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>3.750000</td>\n",
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              "      <td>10.000000</td>\n",
              "      <td>352.000000</td>\n",
              "      <td>7.00000</td>\n",
              "      <td>22.983299</td>\n",
              "      <td>1.009650</td>\n",
              "      <td>318.778455</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>51.239831</td>\n",
              "      <td>43.500000</td>\n",
              "      <td>392.500000</td>\n",
              "      <td>6006.352023</td>\n",
              "      <td>47.500000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.320534</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>568.500000</td>\n",
              "      <td>14.00000</td>\n",
              "      <td>45.905622</td>\n",
              "      <td>2.141863</td>\n",
              "      <td>520.430444</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>77.198228</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>704.250000</td>\n",
              "      <td>8253.976921</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>71.250000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>7.601695</td>\n",
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              "      <td>797.000000</td>\n",
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              "      <td>68.621026</td>\n",
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              "      <td>30.000000</td>\n",
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              "      <td>10.000000</td>\n",
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              "      <td>30.000000</td>\n",
              "      <td>985.000000</td>\n",
              "      <td>30.00000</td>\n",
              "      <td>99.466109</td>\n",
              "      <td>4.939255</td>\n",
              "      <td>997.413450</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Product type'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1web8rH4l9Y9",
        "outputId": "7372b539-9be8-440c-fdef-1926d2e03bab"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['haircare', 'skincare', 'cosmetics'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Transportation modes'].mode()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zocf3RlDmw7E",
        "outputId": "f10ef961-f4da-416b-cd11-22daf0477086"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Road\n",
              "Name: Transportation modes, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Transportation modes'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QyywoHbfm-vt",
        "outputId": "774faf47-ef9f-4879-f503-40b3cf070264"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Road', 'Air', 'Rail', 'Sea'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Product Type and Price**\n",
        "#####  analyzing the Supply Chain by looking at the relationship between the price of the products and the revenue generated by them\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "Wx9G4HE8nC-M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.scatter(data, x='Price',\n",
        "                 y='Revenue generated',\n",
        "                 color='Product type',\n",
        "                 hover_data=['Number of products sold'],\n",
        "                 trendline=\"ols\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "iH-rfiPJo-3d",
        "outputId": "d4421188-aa61-44c3-eb27-fbe0125ef491"
      },
      "execution_count": 11,
      "outputs": [
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    },
    {
      "cell_type": "markdown",
      "source": [
        " The company derives more revenue from skincare products, and the higher the price of skincare products, the more revenue they generate\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "u_aFOIGAp9RP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Sales by Product Type**\n"
      ],
      "metadata": {
        "id": "9PzlHNlWqRr8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sales_data = data.groupby('Product type')['Number of products sold'].sum().reset_index()\n",
        "sales_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "Rtt2pwLjqkge",
        "outputId": "db61d601-a521-43d2-ba99-0b121c0777eb"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Product type  Number of products sold\n",
              "0    cosmetics                    11757\n",
              "1     haircare                    13611\n",
              "2     skincare                    20731"
            ],
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-e37a7c12-2026-432d-a3eb-a2dfda1f6c56 button');\n",
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              "  </div>\n"
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          },
          "metadata": {},
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    {
      "cell_type": "code",
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        "\n",
        "pie_chart = px.pie(sales_data, values='Number of products sold', names='Product type',\n",
        "                   title='Sales by Product Type',\n",
        "                   hover_data=['Number of products sold'],\n",
        "                   hole=0.5,\n",
        "                   color_discrete_sequence=px.colors.qualitative.Pastel)\n",
        "\n",
        "pie_chart.update_traces(textposition='inside', textinfo='percent+label')\n",
        "pie_chart.show()"
      ],
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      "execution_count": 29,
      "outputs": [
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      "source": [
        "So 45% of the business comes from skincare products, 29.5% from haircare, and 25.5% from cosmetics\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "ofLO9whKuHOb"
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    {
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        "customer_data = data.groupby('Customer demographics')['Number of products sold'].sum().reset_index()\n",
        "customer_data"
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      "execution_count": 25,
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              "  Customer demographics  Number of products sold\n",
              "0                Female                    12801\n",
              "1                  Male                     7507\n",
              "2            Non-binary                    10580\n",
              "3               Unknown                    15211"
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              "      --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",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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",
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              "  }\n",
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              "      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",
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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-e7393c60-29ec-45cb-9b12-114e26d4b9a0 button');\n",
              "      quickchartButtonEl.style.display =\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
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        "pie_chart = px.pie(customer_data, values='Number of products sold', names='Customer demographics',\n",
        "                   title='Sales by Customer',\n",
        "                   hover_data=['Number of products sold'],\n",
        "                   hole=0.5,\n",
        "                   color_discrete_sequence=px.colors.qualitative.G10)\n",
        "\n",
        "pie_chart.update_traces(textposition='inside', textinfo='percent+label')\n",
        "pie_chart.show()"
      ],
      "metadata": {
        "colab": {
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          "height": 542
        },
        "id": "O-aqgsSSt73F",
        "outputId": "c3e72351-0210-47b7-a615-6f80a25ff6b0"
      },
      "execution_count": 28,
      "outputs": [
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Total Revenue by Shipping Carrier**"
      ],
      "metadata": {
        "id": "SqOCTgQ5xziQ"
      }
    },
    {
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      "source": [
        "total_revenue = data.groupby('Shipping carriers')['Revenue generated'].sum().reset_index()\n",
        "total_revenue"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "7y52eWxox-V6",
        "outputId": "b6f849a0-83d7-450b-d2a3-4c1802bb9fb9"
      },
      "execution_count": 12,
      "outputs": [
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            "text/plain": [
              "  Shipping carriers  Revenue generated\n",
              "0         Carrier A      142629.994607\n",
              "1         Carrier B      250094.646988\n",
              "2         Carrier C      184880.177143"
            ],
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7ecd237e-6e36-454b-9b95-33e214836d48 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-7ecd237e-6e36-454b-9b95-33e214836d48');\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-976180f4-6061-4bef-b622-052e33ab1eb4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-976180f4-6061-4bef-b622-052e33ab1eb4')\"\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-976180f4-6061-4bef-b622-052e33ab1eb4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(total_revenue,x='Shipping carriers',y='Revenue generated',palette=\"blend:#7AB,#EDA\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "74f8YuBMyN-R",
        "outputId": "4f148fab-c9e8-4b45-e4a2-857c6fe6d7d7"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Shipping carriers', ylabel='Revenue generated'>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####The company is using three carriers for transportation, and Carrier B helps the company in generating more revenue.\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "hojJe10vmVhX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Total Revenue based on Location**"
      ],
      "metadata": {
        "id": "qmuerBIPfke4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "total_revenue = data.groupby('Location')['Revenue generated'].sum().reset_index()\n",
        "total_revenue"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YY39nM0zg0AE",
        "outputId": "e535a4f5-a63d-4ecf-cb0f-36bafc275c82"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Location  Revenue generated\n",
              "0  Bangalore      102601.723882\n",
              "1    Chennai      119142.815748\n",
              "2      Delhi       81027.701225\n",
              "3    Kolkata      137077.551005\n",
              "4     Mumbai      137755.026877"
            ],
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            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(11,4))\n",
        "sns.barplot(total_revenue,x='Revenue generated',y='Location',palette='flare')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "EjCQxz-OiGXu",
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      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Revenue generated', ylabel='Location'>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### So the products  are being shipped to Mumbai helps the company in generating more revenue.\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "vI3CylI8n6_B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Product Type**\n",
        "### Let’s have a look at the Average lead time and Average Manufacturing Costs for all products of the company:"
      ],
      "metadata": {
        "id": "Ms-_-PJmk_z0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "avg_lead_time = data.groupby('Product type')['Lead time'].mean().reset_index()\n",
        "avg_manufacturing_costs = data.groupby('Product type')['Manufacturing costs'].mean().reset_index()\n",
        "result = pd.merge(avg_lead_time, avg_manufacturing_costs, on='Product type')\n",
        "result.rename(columns={'Lead time': 'Average Lead Time', 'Manufacturing costs': 'Average Manufacturing Costs'}, inplace=True)\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "G3SIwFUIlMHY",
        "outputId": "73a75e37-f6ce-4f8c-86f1-cf83298b03f9"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Product type  Average Lead Time  Average Manufacturing Costs\n",
              "0    cosmetics          13.538462                    43.052740\n",
              "1     haircare          18.705882                    48.457993\n",
              "2     skincare          18.000000                    48.993157"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a5a642ef-7aba-4abf-bc4e-742869980355\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Product type</th>\n",
              "      <th>Average Lead Time</th>\n",
              "      <th>Average Manufacturing Costs</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>cosmetics</td>\n",
              "      <td>13.538462</td>\n",
              "      <td>43.052740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>haircare</td>\n",
              "      <td>18.705882</td>\n",
              "      <td>48.457993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>skincare</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>48.993157</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a5a642ef-7aba-4abf-bc4e-742869980355')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a5a642ef-7aba-4abf-bc4e-742869980355 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-a5a642ef-7aba-4abf-bc4e-742869980355');\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-a83f9f8d-79f1-43d7-a0ea-09ff35b1f28c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a83f9f8d-79f1-43d7-a0ea-09ff35b1f28c')\"\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-a83f9f8d-79f1-43d7-a0ea-09ff35b1f28c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Analyzing SKUs**\n",
        "\n",
        "### SKU stands for Stock Keeping Units. They’re like special codes that help companies keep track of all the different things they have for sale.Let’s analyze the revenue generated by each SKU:"
      ],
      "metadata": {
        "id": "Nx_M3c3ypMGd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Revenue generated by SKUs**"
      ],
      "metadata": {
        "id": "W6d9Trh5shnb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "revenue_chart = px.line(data, x='SKU',\n",
        "                        y='Revenue generated',\n",
        "                        title='Revenue Generated by SKU')\n",
        "revenue_chart.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "aa69gDPssBQu",
        "outputId": "3bbb4adc-0387-49c1-fbf7-bf8af41321b3"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Stock Levels by SKUs**\n",
        "###Stock levels refer to the number of products a store or business has in its inventory. Now let’s have a look at the stock levels of each SKU:\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ytnqQR5NstwW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stock_chart = px.line(data, x='SKU',\n",
        "                      y='Stock levels',\n",
        "                      title='Stock Levels by SKU')\n",
        "stock_chart.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "S1Xvj1-tsRil",
        "outputId": "9f279ff4-1f07-4c6b-c62e-2a0ce61b0147"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "            Plotly.purge(gd);\n",
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              "        }}\n",
              "}});\n",
              "\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Order Quantity by SKUs**"
      ],
      "metadata": {
        "id": "2LkmJhBUtQAP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "order_quantity_chart = px.bar(data, x='SKU',\n",
        "                              y='Order quantities',\n",
        "                              title='Order Quantity by SKU')\n",
        "order_quantity_chart.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "3OL40J9wtZQB",
        "outputId": "c2c54b9d-3f61-4226-9fee-98ed4be84612"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<body>\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Cost Analysis**\n",
        "### Now let’s analyze the shipping cost of Carriers:\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "4TLm2dq0tq9a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "shipping_cost_chart = px.bar(data, x='Shipping carriers',\n",
        "                             y='Shipping costs',\n",
        "                             title='Shipping Costs by Carrier')\n",
        "shipping_cost_chart.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "HfHHey1JtzRr",
        "outputId": "b14ed5a6-6742-4a15-8d38-326ee3e04779"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
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carriers\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Shipping costs\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Shipping Costs by Carrier\"},\"barmode\":\"relative\"},                        {\"responsive\": true}                    ).then(function(){\n",
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          "metadata": {}
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    {
      "cell_type": "markdown",
      "source": [
        "####In one of the above visualizations, we discovered that Carrier B helps the company in more revenue. It is also the most costly Carrier among the three.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "aCKwLGL8t-EZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Cost Distribution by Transportation Mode**"
      ],
      "metadata": {
        "id": "1fRyjukHuT17"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "transportation_chart = px.pie(data,\n",
        "                              values='Costs',\n",
        "                              names='Transportation modes',\n",
        "                              title='Cost Distribution by Transportation Mode',\n",
        "                              hole=0.5,\n",
        "                              color_discrete_sequence=px.colors.qualitative.Pastel)\n",
        "transportation_chart.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "l9lD1mEMunay",
        "outputId": "da91a226-fcba-4142-f468-f92ed56b5914"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
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    {
      "cell_type": "markdown",
      "source": [
        "####So the company spends more on Road and Rail modes of transportation for the transportation of Goods.\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "IUIkWEnOuwKH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Analyzing Defect Rate**\n",
        "\n",
        "####The defect rate in the supply chain refers to the percentage of products that have something wrong or are found broken after shipping. Let’s have a look at the average defect rate of all product types:"
      ],
      "metadata": {
        "id": "1bgdkD09uz_s"
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    {
      "cell_type": "code",
      "source": [
        "defect_rates_by_product = data.groupby('Product type')['Defect rates'].mean().reset_index()\n",
        "\n",
        "fig = px.bar(defect_rates_by_product, x='Product type', y='Defect rates',\n",
        "             title='Average Defect Rates by Product Type')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "EbxJPWo6vKWt",
        "outputId": "cfaf20fe-0f14-457c-d174-daf96eeb028a"
      },
      "execution_count": 38,
      "outputs": [
        {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####So the defect rate of haircare products is higher.\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "RrGaRfrNvj8d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Defect Rates by Mode of Transportation**"
      ],
      "metadata": {
        "id": "hM9Vbg3Ovt-c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pivot_table = pd.pivot_table(data, values='Defect rates',\n",
        "                             index=['Transportation modes'],\n",
        "                             aggfunc='mean')\n",
        "\n",
        "transportation_chart = px.pie(values=pivot_table[\"Defect rates\"],\n",
        "                              names=pivot_table.index,\n",
        "                              title='Defect Rates by Transportation Mode',\n",
        "                              hole=0.5,\n",
        "                              color_discrete_sequence=px.colors.qualitative.Pastel)\n",
        "transportation_chart.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "azaGfxhyvouF",
        "outputId": "dd41da1e-2cba-41fa-d394-146c6701df24"
      },
      "execution_count": 39,
      "outputs": [
        {
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    {
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      "source": [
        "####Road transportation results in a higher defect rate, and Air transportation has the lowest defect rate.\n",
        "\n",
        "\n",
        "---\n",
        "\n"
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