theLook_rfm_delivery_time_series_analysis.ipynb

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      "cell_type": "markdown",
      "source": [
        "# theLook ecommerce project"
      ],
      "metadata": {
        "id": "d26sEd_MxZH7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "!pip install squarify\n",
        "import squarify\n",
        "import warnings\n"
      ],
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      },
      "execution_count": 45,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Requirement already satisfied: squarify in /usr/local/lib/python3.10/dist-packages (0.4.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. RFM analysis (2019~2024)"
      ],
      "metadata": {
        "id": "NoxMp5w6w1Bz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![RFM metrics](https://clevertap.com/wp-content/uploads/2018/03/Incontent_image.png)"
      ],
      "metadata": {
        "id": "peXK4XtVzjlj"
      }
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      "source": [
        "# import data\n",
        "url_rfm_2019_2024 ='https://raw.githubusercontent.com/Taweilo/theLook_ecommerce/main/Data/rfm_2019_2024_data.csv'\n",
        "rfm_df = pd.read_csv(url_rfm_2019_2024)\n",
        "rfm_df.head()"
      ],
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          "height": 206
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        "outputId": "a76530b1-8663-4b8b-c37c-9c02a3eeeaf9"
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      "execution_count": 46,
      "outputs": [
        {
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              "   user_id  recency  frequency    monetary\n",
              "0    11291      384          7  426.850002\n",
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              "2    49853      245          7  418.520002\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "rfm_df",
              "summary": "{\n  \"name\": \"rfm_df\",\n  \"rows\": 27519,\n  \"fields\": [\n    {\n      \"column\": \"user_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28830,\n        \"min\": 1,\n        \"max\": 100000,\n        \"num_unique_values\": 27519,\n        \"samples\": [\n          69079,\n          32815,\n          52567\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 414,\n        \"min\": -4,\n        \"max\": 1897,\n        \"num_unique_values\": 1797,\n        \"samples\": [\n          1198,\n          820,\n          1044\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          4,\n          9,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 103.99480869515672,\n        \"min\": 0.4900000095367431,\n        \"max\": 1353.829999923706,\n        \"num_unique_values\": 10060,\n        \"samples\": [\n          308.8399963378906,\n          25.209999084472656,\n          297.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "print(rfm_df.shape)\n",
        "print(\" \")\n",
        "print(rfm_df.info())"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u-_UcKmO11Zo",
        "outputId": "46d56afe-ddd8-4e29-a0dc-59b2fbf4c1d0"
      },
      "execution_count": 47,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "(27519, 4)\n",
            " \n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 27519 entries, 0 to 27518\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   user_id    27519 non-null  int64  \n",
            " 1   recency    27519 non-null  int64  \n",
            " 2   frequency  27519 non-null  int64  \n",
            " 3   monetary   27519 non-null  float64\n",
            "dtypes: float64(1), int64(3)\n",
            "memory usage: 860.1 KB\n",
            "None\n"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "# Plotting RFM distributions\n",
        "plt.figure(figsize=(12,10))\n",
        "# Plot distribution of R\n",
        "plt.subplot(3, 1, 1); sns.distplot(rfm_df['recency'])\n",
        "# Plot distribution of F\n",
        "plt.subplot(3, 1, 2); sns.distplot(rfm_df['frequency'])\n",
        "# Plot distribution of M\n",
        "plt.subplot(3, 1, 3); sns.distplot(rfm_df['monetary'])\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
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          "height": 850
        },
        "id": "Jc5wHtnWu0u9",
        "outputId": "af9f688c-a2d4-4b2e-e066-75139d57bd43"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 3 Axes>"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rfm_df[\"R\"] = pd.qcut(rfm_df['recency'], 5, labels=[5, 4, 3, 2, 1])\n",
        "rfm_df[\"F\"] = pd.qcut(rfm_df[\"frequency\"].rank(method=\"first\"), 5, labels=[1, 2, 3, 4, 5])\n",
        "rfm_df[\"M\"] = pd.qcut(rfm_df[\"monetary\"], 5, labels=[1, 2, 3, 4, 5])\n",
        "rfm_df[\"rfm_df_Score\"] = rfm_df[\"R\"].astype(str) + rfm_df[\"F\"].astype(str) + rfm_df[\"M\"].astype(str)"
      ],
      "metadata": {
        "id": "8g1gXcZ82FJ4"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "| Segment                   | Description                                                                                      |\n",
        "|---------------------------|--------------------------------------------------------------------------------------------------|\n",
        "| Champions                 | Bought recently, buy often, and spend the most.                                                  |\n",
        "| Loyal Customers           | Buy on a regular basis and are responsive to promotions.                                         |\n",
        "| Potential Loyalist        | Recent customers with average frequency.                                                         |\n",
        "| Recent Customers          | Bought most recently but not often.                                                               |\n",
        "| Promising                 | Recent shoppers who haven't spent much.                                                           |\n",
        "| Customers Needing Attention | Above-average recency, frequency, and monetary values. May not have bought very recently.      |\n",
        "| About To Sleep            | Below-average recency and frequency. Will lose them if not reactivated.                           |\n",
        "| At Risk                   | Purchased often but a long time ago. Need to bring them back!                                     |\n",
        "| Can’t Lose Them           | Used to purchase frequently but haven’t returned for a long time.                                  |\n",
        "| Hibernating               | Last purchase was a long time ago with a low number of orders. May be lost.                        |\n"
      ],
      "metadata": {
        "id": "ob3FoQARBAQK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "seg_map = {\n",
        "    r'[1-2][1-2]': 'Hibernating',\n",
        "    r'[1-2][3-4]': 'At Risk',\n",
        "    r'[1-2]5': 'Can\\'t Lose',\n",
        "    r'3[1-2]': 'About to Sleep',\n",
        "    r'33': 'Need Attention',\n",
        "    r'[3-4][4-5]': 'Loyal Customers',\n",
        "    r'41': 'Promising',\n",
        "    r'51': 'New Customers',\n",
        "    r'[4-5][2-3]': 'Potential Loyalists',\n",
        "    r'5[4-5]': 'Champions'\n",
        "}"
      ],
      "metadata": {
        "id": "c6hByjvt5s_U"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "![RFM treemap](https://guillaume-martin.github.io/images/rfm-segments.png)"
      ],
      "metadata": {
        "id": "gqbMTs9WAmG5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rfm_df['segment'] = rfm_df['R'].astype(str) + rfm_df['F'].astype(str)\n",
        "rfm_df['segment'] = rfm_df['segment'].replace(seg_map, regex=True)\n",
        "rfm_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6151J-Yz57zF",
        "outputId": "c972d2a8-a710-4f60-d371-af7e6807b74f"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   user_id  recency  frequency    monetary  R  F  M rfm_df_Score  \\\n",
              "0    11291      384          7  426.850002  3  5  5          355   \n",
              "1    97434       73          9  497.770002  4  5  5          455   \n",
              "2    49853      245          7  418.520002  3  5  5          355   \n",
              "3    64662      224          6  139.330002  3  5  4          354   \n",
              "4    21679      433          7  324.959996  3  5  5          355   \n",
              "\n",
              "           segment  \n",
              "0  Loyal Customers  \n",
              "1  Loyal Customers  \n",
              "2  Loyal Customers  \n",
              "3  Loyal Customers  \n",
              "4  Loyal Customers  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-46f3beb3-9135-4c6d-a06e-5b64bf11220d\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>recency</th>\n",
              "      <th>frequency</th>\n",
              "      <th>monetary</th>\n",
              "      <th>R</th>\n",
              "      <th>F</th>\n",
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              "      <th>0</th>\n",
              "      <td>11291</td>\n",
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              "      <td>426.850002</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>355</td>\n",
              "      <td>Loyal Customers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>97434</td>\n",
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              "      <td>497.770002</td>\n",
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              "      <td>455</td>\n",
              "      <td>Loyal Customers</td>\n",
              "    </tr>\n",
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              "      <td>5</td>\n",
              "      <td>355</td>\n",
              "      <td>Loyal Customers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>64662</td>\n",
              "      <td>224</td>\n",
              "      <td>6</td>\n",
              "      <td>139.330002</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>354</td>\n",
              "      <td>Loyal Customers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>21679</td>\n",
              "      <td>433</td>\n",
              "      <td>7</td>\n",
              "      <td>324.959996</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>355</td>\n",
              "      <td>Loyal Customers</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "rfm_df",
              "summary": "{\n  \"name\": \"rfm_df\",\n  \"rows\": 27519,\n  \"fields\": [\n    {\n      \"column\": \"user_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28830,\n        \"min\": 1,\n        \"max\": 100000,\n        \"num_unique_values\": 27519,\n        \"samples\": [\n          69079,\n          32815,\n          52567\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 414,\n        \"min\": -4,\n        \"max\": 1897,\n        \"num_unique_values\": 1797,\n        \"samples\": [\n          1198,\n          820,\n          1044\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"frequency\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          4,\n          9,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"monetary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 103.99480869515672,\n        \"min\": 0.4900000095367431,\n        \"max\": 1353.829999923706,\n        \"num_unique_values\": 10060,\n        \"samples\": [\n          308.8399963378906,\n          25.209999084472656,\n          297.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rfm_df_Score\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 80,\n        \"samples\": [\n          \"333\",\n          \"355\",\n          \"423\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"segment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"At Risk\",\n          \"Champions\",\n          \"Hibernating\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the desired order of segments\n",
        "segment_order = [\n",
        "    'Champions', 'Loyal Customers', 'Potential Loyalists', 'New Customers',\n",
        "    'Promising', 'Need Attention', 'About to Sleep', 'At Risk',\n",
        "    'Can\\'t Lose', 'Hibernating'\n",
        "]\n",
        "\n",
        "# Group by segment and calculate aggregates\n",
        "rfm_grouped = rfm_df.groupby('segment').agg({'user_id': lambda x: ', '.join(x.astype(str)), 'recency': 'mean', 'frequency': 'mean', 'monetary': 'mean'})\n",
        "rfm_grouped['user_count'] = rfm_df.groupby('segment')['user_id'].size()\n",
        "rfm_grouped['percent'] = round((rfm_grouped['user_count']/rfm_grouped.user_count.sum())*100, 2)\n",
        "\n",
        "# Reorder the segments based on segment_order\n",
        "rfm_grouped = rfm_grouped.reindex(segment_order)\n",
        "\n",
        "# Round 'recency', 'frequency', and 'monetary' columns to 2 digits\n",
        "rfm_grouped[['recency', 'frequency', 'monetary']] = rfm_grouped[['recency', 'frequency', 'monetary']].round(2)\n",
        "\n",
        "# Move the 'user_id' column to the end\n",
        "rfm_grouped = rfm_grouped[['recency', 'frequency', 'monetary', 'user_count', 'percent', 'user_id']]\n",
        "\n",
        "# Display the reordered and rounded DataFrame\n",
        "rfm_grouped\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "EDszS0mVkVjm",
        "outputId": "720a4b82-3ec3-46ac-e293-611026334d64"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     recency  frequency  monetary  user_count  percent  \\\n",
              "segment                                                                  \n",
              "Champions              27.49       2.65    167.26        2442     8.87   \n",
              "Loyal Customers       226.58       2.67    168.65        4511    16.39   \n",
              "Potential Loyalists    85.53       1.00     69.58        4158    15.11   \n",
              "New Customers          27.26       1.00     18.02         995     3.62   \n",
              "Promising             139.06       1.00     17.95        1108     4.03   \n",
              "Need Attention        317.65       1.00    100.78        1073     3.90   \n",
              "About to Sleep        319.87       1.00     29.08        2234     8.12   \n",
              "At Risk               861.36       1.44    108.38        4486    16.30   \n",
              "Can't Lose            834.81       3.26    215.05        1895     6.89   \n",
              "Hibernating           877.86       1.00     29.25        4617    16.78   \n",
              "\n",
              "                                                               user_id  \n",
              "segment                                                                 \n",
              "Champions            65423, 84596, 94492, 81887, 4104, 37155, 92227...  \n",
              "Loyal Customers      11291, 97434, 49853, 64662, 21679, 64470, 9018...  \n",
              "Potential Loyalists  6898, 11238, 15115, 16364, 17300, 29771, 33477...  \n",
              "New Customers        9618, 13757, 5462, 66952, 88431, 11937, 77055,...  \n",
              "Promising            94582, 63005, 93890, 38293, 24855, 37516, 1871...  \n",
              "Need Attention       43253, 44169, 57752, 62966, 64260, 71253, 7196...  \n",
              "About to Sleep       87818, 929, 41414, 58741, 60485, 53355, 59797,...  \n",
              "At Risk              38752, 40419, 42677, 43492, 47749, 48820, 5579...  \n",
              "Can't Lose           62645, 65964, 62998, 14745, 4966, 15906, 6327,...  \n",
              "Hibernating          28769, 19284, 11621, 20025, 70392, 20910, 2606...  "
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>recency</th>\n",
              "      <th>frequency</th>\n",
              "      <th>monetary</th>\n",
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              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Champions</th>\n",
              "      <td>27.49</td>\n",
              "      <td>2.65</td>\n",
              "      <td>167.26</td>\n",
              "      <td>2442</td>\n",
              "      <td>8.87</td>\n",
              "      <td>65423, 84596, 94492, 81887, 4104, 37155, 92227...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Loyal Customers</th>\n",
              "      <td>226.58</td>\n",
              "      <td>2.67</td>\n",
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              "      <td>4511</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Potential Loyalists</th>\n",
              "      <td>85.53</td>\n",
              "      <td>1.00</td>\n",
              "      <td>69.58</td>\n",
              "      <td>4158</td>\n",
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              "      <td>6898, 11238, 15115, 16364, 17300, 29771, 33477...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>New Customers</th>\n",
              "      <td>27.26</td>\n",
              "      <td>1.00</td>\n",
              "      <td>18.02</td>\n",
              "      <td>995</td>\n",
              "      <td>3.62</td>\n",
              "      <td>9618, 13757, 5462, 66952, 88431, 11937, 77055,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Promising</th>\n",
              "      <td>139.06</td>\n",
              "      <td>1.00</td>\n",
              "      <td>17.95</td>\n",
              "      <td>1108</td>\n",
              "      <td>4.03</td>\n",
              "      <td>94582, 63005, 93890, 38293, 24855, 37516, 1871...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Need Attention</th>\n",
              "      <td>317.65</td>\n",
              "      <td>1.00</td>\n",
              "      <td>100.78</td>\n",
              "      <td>1073</td>\n",
              "      <td>3.90</td>\n",
              "      <td>43253, 44169, 57752, 62966, 64260, 71253, 7196...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>About to Sleep</th>\n",
              "      <td>319.87</td>\n",
              "      <td>1.00</td>\n",
              "      <td>29.08</td>\n",
              "      <td>2234</td>\n",
              "      <td>8.12</td>\n",
              "      <td>87818, 929, 41414, 58741, 60485, 53355, 59797,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>At Risk</th>\n",
              "      <td>861.36</td>\n",
              "      <td>1.44</td>\n",
              "      <td>108.38</td>\n",
              "      <td>4486</td>\n",
              "      <td>16.30</td>\n",
              "      <td>38752, 40419, 42677, 43492, 47749, 48820, 5579...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Can't Lose</th>\n",
              "      <td>834.81</td>\n",
              "      <td>3.26</td>\n",
              "      <td>215.05</td>\n",
              "      <td>1895</td>\n",
              "      <td>6.89</td>\n",
              "      <td>62645, 65964, 62998, 14745, 4966, 15906, 6327,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hibernating</th>\n",
              "      <td>877.86</td>\n",
              "      <td>1.00</td>\n",
              "      <td>29.25</td>\n",
              "      <td>4617</td>\n",
              "      <td>16.78</td>\n",
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              "\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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              "      const buttonEl =\n",
              "        document.querySelector('#df-4e1cded3-e220-4ce3-9b62-e9ff8bee5a39 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-4e1cded3-e220-4ce3-9b62-e9ff8bee5a39');\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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              "      --bg-color: #3B4455;\n",
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              "  .colab-df-quickchart {\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",
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              "\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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              "      border-top-color: var(--fill-color);\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",
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              "      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",
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              "    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-025a4d21-0d80-456d-a747-4d199b7a3c8b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_094d7135-ed02-4763-8111-89076d79523f\">\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        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('rfm_grouped')\"\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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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        google.colab.notebook.generateWithVariable('rfm_grouped');\n",
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            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create color dic for the treemap\n",
        "colors_dict = {\n",
        "    'Champions': 'yellow',\n",
        "    'Loyal Customers': 'royalblue',\n",
        "    'Potential Loyalists': 'cyan',\n",
        "    'New Customers': 'red',\n",
        "    'Promising': 'purple',\n",
        "    'Need Attention': 'green',\n",
        "    'About to Sleep': 'gold',\n",
        "    'At Risk': 'orange',\n",
        "    \"Can't Lose\": 'pink',\n",
        "    'Hibernating': 'brown'\n",
        "}\n",
        "\n",
        "# Create the plot and resize it\n",
        "fig = plt.figure(figsize=(12, 10))\n",
        "ax = fig.add_subplot()\n",
        "\n",
        "# Plot the treemap with labels and colors\n",
        "squarify.plot(sizes=rfm_grouped['user_count'],\n",
        "              #text_kwargs={'fontsize': 8, 'weight': 'bold', 'fontname': \"sans serif\"},\n",
        "              color=[colors_dict[segment] for segment in rfm_grouped.index],\n",
        "              label=['{} \\n{:.1f} days \\n{:.1f} orders \\n${:.1f} \\n{:.0f} customers ({:.0f}%)'.format(*row)\n",
        "                     for row in rfm_grouped.itertuples()], alpha=0.5)\n",
        "\n",
        "plt.title(\"Customers Segments\", fontsize=20)\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 837
        },
        "id": "MPr8L1Novoap",
        "outputId": "d3783222-eb80-4614-c929-52e1ded543c2"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![action](https://miro.medium.com/v2/resize:fit:1400/format:webp/0*pGS1-NwGc_87IBtY)"
      ],
      "metadata": {
        "id": "h53xV5O8HoiP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://guillaume-martin.github.io/rfm-segmentation-with-python.html\n",
        "\n",
        "https://medium.com/@ogunbajoadeyinka/building-an-rfm-model-in-python-3481112a2816\n",
        "\n",
        "https://melodyyip.github.io/RFM-UCI-onlineStore/\n",
        "\n",
        "https://github.com/amr-khalil/Identify-customer-segmentation/blob/master/RFM%20Final.ipynb\n",
        "\n",
        "https://github.com/klaudia-nazarko/rfm-analysis-python/blob/master/rfm_analysis_python.ipynb\n",
        "\n",
        "https://medium.com/@ugursavci/customer-segmentation-using-rfm-analysis-in-python-218a3255f714 <br>\n",
        "https://clevertap.com/blog/rfm-analysis/?source=post_page-----a33fa287c0e--------------------------------"
      ],
      "metadata": {
        "id": "1hxxbWXoG0hR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. Delivery analysis (2023 reevaluation)"
      ],
      "metadata": {
        "id": "1KZoLbY5xOuC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing the libraries\n",
        "import geopandas as gpd\n",
        "from geopandas import GeoDataFrame\n",
        "from shapely.geometry import Point\n",
        "import warnings\n",
        "from sklearn.cluster import KMeans\n",
        "!pip install pyclustering\n",
        "from pyclustering.cluster.kmedians import kmedians\n",
        "from math import radians, sin, cos, sqrt, atan2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RNq8xuvBo3bV",
        "outputId": "d6f26d8a-9ed3-46f6-d2c3-c142fea4378d"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pyclustering in /usr/local/lib/python3.10/dist-packages (0.10.1.2)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from pyclustering) (1.11.4)\n",
            "Requirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from pyclustering) (3.7.1)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import data\n",
        "url_domestic_2023_delivery ='https://raw.githubusercontent.com/Taweilo/theLook_ecommerce/main/Data/us_delivery_2023_data.csv'\n",
        "domestic_delivery_df = pd.read_csv(url_domestic_2023_delivery)\n",
        "domestic_delivery_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "jfvR0dsDxXoi",
        "outputId": "9e389796-1657-4157-be9a-0576ef0a9f2f"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   item_id  customer_postal_code        country       state          city  \\\n",
              "0    32591                 53716  United States   Wisconsin       Madison   \n",
              "1    42105                 92008  United States  California      Carlsbad   \n",
              "2   180392                 64804  United States    Missouri        Joplin   \n",
              "3    94138                 37604  United States   Tennessee  Johnson City   \n",
              "4   145578                 20723  United States    Maryland  North Laurel   \n",
              "\n",
              "   customer_longitude  customer_latitude  lead_time       name  dc_longitude  \\\n",
              "0          -89.331811          43.066362      121.0  Mobile AL      -88.0431   \n",
              "1         -117.318668          33.144374       41.0  Mobile AL      -88.0431   \n",
              "2          -94.510106          37.019306       95.0  Mobile AL      -88.0431   \n",
              "3          -82.382191          36.295325       77.0  Mobile AL      -88.0431   \n",
              "4          -76.865842          39.136438       82.0  Mobile AL      -88.0431   \n",
              "\n",
              "   dc_latitude                    delivered_at  \\\n",
              "0      30.6944  2023-05-07 08:24:00.000000 UTC   \n",
              "1      30.6944  2023-07-12 19:09:00.000000 UTC   \n",
              "2      30.6944  2023-12-06 16:52:00.000000 UTC   \n",
              "3      30.6944  2023-11-23 19:33:00.000000 UTC   \n",
              "4      30.6944  2023-03-02 11:06:00.000000 UTC   \n",
              "\n",
              "                       shipped_at                      created_at  \n",
              "0  2023-05-04 01:28:00.000000 UTC  2023-05-02 07:09:51.000000 UTC  \n",
              "1  2023-07-11 18:19:00.000000 UTC  2023-07-11 01:54:11.000000 UTC  \n",
              "2  2023-12-03 01:39:00.000000 UTC  2023-12-02 17:42:18.000000 UTC  \n",
              "3  2023-11-21 22:13:00.000000 UTC  2023-11-20 14:03:10.000000 UTC  \n",
              "4  2023-03-01 05:02:00.000000 UTC  2023-02-27 00:38:50.000000 UTC  "
            ],
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              "      <td>2023-07-11 18:19:00.000000 UTC</td>\n",
              "      <td>2023-07-11 01:54:11.000000 UTC</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>2023-11-21 22:13:00.000000 UTC</td>\n",
              "      <td>2023-11-20 14:03:10.000000 UTC</td>\n",
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              "summary": "{\n  \"name\": \"domestic_delivery_df\",\n  \"rows\": 3936,\n  \"fields\": [\n    {\n      \"column\": \"item_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52221,\n        \"min\": 20,\n        \"max\": 181839,\n        \"num_unique_values\": 3936,\n        \"samples\": [\n          111243,\n          38460,\n          56145\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_postal_code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31062,\n        \"min\": 1085,\n        \"max\": 99801,\n        \"num_unique_values\": 1941,\n        \"samples\": [\n          11233,\n          33179,\n          96720\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 49,\n        \"samples\": [\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1195,\n        \"samples\": [\n          \"Jensen Beach\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.954552371368877,\n        \"min\": -158.0411899,\n        \"max\": -70.99304462,\n        \"num_unique_values\": 1941,\n        \"samples\": [\n          -73.91988379\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.317150198481303,\n        \"min\": 19.43480369,\n        \"max\": 64.7485059,\n        \"num_unique_values\": 1941,\n        \"samples\": [\n          40.67832611\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lead_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.57523265007314,\n        \"min\": -89.0,\n        \"max\": 188.0,\n        \"num_unique_values\": 263,\n        \"samples\": [\n          26.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Philadelphia PA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dc_longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.907643718645016,\n        \"min\": -118.25,\n        \"max\": -73.7834,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -75.1667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dc_latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.4224672507901674,\n        \"min\": 29.7604,\n        \"max\": 41.8369,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          39.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"delivered_at\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 2726,\n        \"samples\": [\n          \"2023-09-16 07:30:00.000000 UTC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"shipped_at\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 2724,\n        \"samples\": [\n          \"2023-09-07 23:31:00.000000 UTC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"created_at\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 3935,\n        \"samples\": [\n          \"2023-09-17 08:24:11.000000 UTC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "domestic_df= domestic_delivery_df[['customer_longitude','customer_latitude','dc_longitude','dc_latitude']]\n",
        "domestic_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gbfm-D6TxXy6",
        "outputId": "4fc53a03-c3c0-43e3-e1da-62a65968b17b"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customer_longitude  customer_latitude  dc_longitude  dc_latitude\n",
              "0          -89.331811          43.066362      -88.0431      30.6944\n",
              "1         -117.318668          33.144374      -88.0431      30.6944\n",
              "2          -94.510106          37.019306      -88.0431      30.6944\n",
              "3          -82.382191          36.295325      -88.0431      30.6944\n",
              "4          -76.865842          39.136438      -88.0431      30.6944"
            ],
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              "      <th>customer_longitude</th>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "domestic_df",
              "summary": "{\n  \"name\": \"domestic_df\",\n  \"rows\": 3936,\n  \"fields\": [\n    {\n      \"column\": \"customer_longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.954552371368877,\n        \"min\": -158.0411899,\n        \"max\": -70.99304462,\n        \"num_unique_values\": 1941,\n        \"samples\": [\n          -73.91988379,\n          -80.20438947,\n          -155.2352026\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.317150198481303,\n        \"min\": 19.43480369,\n        \"max\": 64.7485059,\n        \"num_unique_values\": 1941,\n        \"samples\": [\n          40.67832611,\n          25.93230095,\n          19.71615696\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dc_longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.907643718645016,\n        \"min\": -118.25,\n        \"max\": -73.7834,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -75.1667,\n          -87.6847,\n          -79.9333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dc_latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.4224672507901674,\n        \"min\": 29.7604,\n        \"max\": 41.8369,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          39.95,\n          41.8369,\n          32.7833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Point geometries for customer and distribution center locations\n",
        "geometry = [Point(xy) for xy in zip(domestic_df['customer_longitude'], domestic_df['customer_latitude'])]\n",
        "domestic_df_gdf = GeoDataFrame(domestic_df, geometry=geometry)\n",
        "dc_geometry = [Point(xy) for xy in zip(domestic_df['dc_longitude'], domestic_df['dc_latitude'])]\n",
        "domestic_df_gdf_dc = GeoDataFrame(domestic_df, geometry=dc_geometry)\n",
        "\n",
        "# Filter for US territory\n",
        "us = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))[gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))['name'] == 'United States of America']\n",
        "\n",
        "# Plot the US territory with light blue color and overlay customer points\n",
        "ax = us.plot(figsize=(12, 10), facecolor='lightblue')  # Set facecolor to light blue\n",
        "domestic_df_gdf.plot(ax=ax, color='blue', markersize=10, label='Customer Location')  # Add label for legend\n",
        "domestic_df_gdf_dc.plot(ax=ax, color='orange', markersize=10, label='Distribution Center')  # Add label for legend\n",
        "\n",
        "# Add legend\n",
        "plt.legend()\n",
        "\n",
        "# Adjust title and display the plot\n",
        "plt.title('Customer Locations and Distribution Centers in the US')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "oqxYalhXSCC0",
        "outputId": "1c97c5c1-d7ee-444d-a9ee-347544f716e5"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cluster_cols = ['customer_longitude', 'customer_latitude']\n",
        "df_clus=domestic_df[cluster_cols]\n",
        "\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")  # Ignore warning messages\n",
        "\n",
        "cluster_range = range(1,51)\n",
        "cluster_wss = []\n",
        "\n",
        "for num_cluster in cluster_range:\n",
        "    clusters = KMeans(num_cluster)\n",
        "    clusters.fit(df_clus)\n",
        "    cluster_wss.append(clusters.inertia_)\n",
        "\n",
        "fig = plt.figure(figsize=(12, 10))\n",
        "plt.xlabel('#No Clusters')\n",
        "plt.ylabel('WSS')\n",
        "plt.plot(cluster_range, cluster_wss, marker = 'o', color='blue')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "9i9gla3MSCUM",
        "outputId": "467071be-1845-468f-ca76-049dad5c8e73"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the columns for clustering\n",
        "cluster_cols = ['customer_longitude', 'customer_latitude']\n",
        "df_clus = domestic_df[cluster_cols]\n",
        "\n",
        "K = 10\n",
        "# Define the initial medians for K-Medians clustering\n",
        "initial_medians = [[-70,45],\n",
        "                   [-80,  45],\n",
        "                   [-90,  45],\n",
        "                   [-100,  45],\n",
        "                   [-110,  45],\n",
        "                   [-120,  45 ],\n",
        "                   [-75,  30],\n",
        "                   [-85,  30 ],\n",
        "                   [-110,   30],\n",
        "                   [-120,  50]]\n",
        "\n",
        "# Create K-Medians instance with initial medians\n",
        "kmedians_instance = kmedians(df_clus.values.tolist(), initial_medians)\n",
        "\n",
        "# Run cluster analysis and obtain results\n",
        "kmedians_instance.process()\n",
        "cluster_instances = kmedians_instance.get_clusters()  # index of data points in each cluster\n",
        "centers = np.array(kmedians_instance.get_medians())\n",
        "\n",
        "# Initialize cluster membership array\n",
        "clusters = np.zeros(len(df_clus))\n",
        "\n",
        "# Label cluster membership for each point\n",
        "for k, cluster_points in enumerate(cluster_instances):\n",
        "    for point_index in cluster_points:\n",
        "        clusters[point_index] = k  # label cluster membership for each point\n",
        "\n",
        "# Create a DataFrame with cluster membership for each point\n",
        "df_clus_with_clusters = df_clus.copy()\n",
        "df_clus_with_clusters['cluster'] = clusters.astype(int)\n",
        "\n",
        "# Create a DataFrame for centroids\n",
        "centroids_df = pd.DataFrame({'customer_longitude': centers[:, 0], 'customer_latitude': centers[:, 1]})\n",
        "centroids_df['cluster'] = np.arange(K)  # Assign cluster numbers to centroids\n",
        "\n",
        "# Concatenate df_clus_with_clusters and centroids_df to include centroids in the DataFrame\n",
        "df_clus_with_centroids = pd.concat([df_clus_with_clusters, centroids_df])\n",
        "\n",
        "# Plot the clusters and centroids\n",
        "fig = plt.figure(figsize=(12, 10))\n",
        "plt.scatter(df_clus_with_centroids['customer_longitude'], df_clus_with_centroids['customer_latitude'], c=df_clus_with_centroids['cluster'])\n",
        "plt.scatter(centers[:, 0], centers[:, 1], c='red', marker='x', s=100)\n",
        "plt.title(\"K-medians Clustering with Centroids\")\n",
        "plt.xlabel(\"Longitude\")\n",
        "plt.ylabel(\"Latitude\")\n",
        "plt.show()\n",
        "\n",
        "# Display df_clus_with_centroids DataFrame\n",
        "print(df_clus_with_centroids)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6StAmsWNoVlR",
        "outputId": "ba062e2b-a908-40f8-84e7-8f2c664b8e97"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    customer_longitude  customer_latitude  cluster\n",
            "0           -89.331811          43.066362        2\n",
            "1          -117.318668          33.144374        8\n",
            "2           -94.510106          37.019306        3\n",
            "3           -82.382191          36.295325        6\n",
            "4           -76.865842          39.136438        0\n",
            "..                 ...                ...      ...\n",
            "5          -122.022538          37.950805        5\n",
            "6           -83.940889          34.088114        6\n",
            "7           -81.429663          28.621742        7\n",
            "8          -117.406469          33.646703        8\n",
            "9          -122.291807          47.647486        9\n",
            "\n",
            "[3946 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Point geometries for customer locations\n",
        "geometry_customers = [Point(xy) for xy in zip(domestic_df['customer_longitude'], domestic_df['customer_latitude'])]\n",
        "domestic_df_gdf_customers = GeoDataFrame(domestic_df, geometry=geometry_customers)\n",
        "\n",
        "# Create Point geometries for distribution centers\n",
        "geometry_centers = [Point(xy) for xy in zip(centers[:, 0], centers[:, 1])]\n",
        "centers_gdf = GeoDataFrame(geometry=geometry_centers)\n",
        "\n",
        "# Filter for US territory\n",
        "us = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))[gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))['name'] == 'United States of America']\n",
        "\n",
        "# Plot the US territory with light blue color and overlay customer points, clusters, and centroids\n",
        "ax = us.plot(figsize=(12, 10), facecolor='lightblue')  # Set facecolor to light blue\n",
        "domestic_df_gdf_customers.plot(ax=ax, color='blue', markersize=10, label='Customer Location')  # Add label for legend\n",
        "centers_gdf.plot(ax=ax, color='red', markersize=100, label='Cluster Centroids')  # Add label for legend\n",
        "domestic_df_gdf_dc.plot(ax=ax, color='green', markersize=100, label='Distribution Center')  # Add label for legend\n",
        "\n",
        "# Add legend\n",
        "plt.legend()\n",
        "\n",
        "# Adjust title and display the plot\n",
        "plt.title('Customer Locations, Clusters, and Centroids in the US')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "fZ8cOHIX5kkv",
        "outputId": "507a223b-e1be-44a6-f12f-1f29ccd72137"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract centroids' longitude, latitude, and cluster numbers\n",
        "centroids_longitude = centers[:, 0]\n",
        "centroids_latitude = centers[:, 1]\n",
        "centroids_cluster = np.arange(K)\n",
        "\n",
        "# Create a DataFrame for centroids with longitude, latitude, and cluster number\n",
        "centroids_df = pd.DataFrame({\n",
        "    'customer_longitude': centroids_longitude,\n",
        "    'customer_latitude': centroids_latitude,\n",
        "    'cluster': centroids_cluster\n",
        "})\n",
        "\n",
        "# Concatenate df_clus_with_clusters and centroids_df to include centroids in the DataFrame\n",
        "df_clus_with_centroids = pd.concat([df_clus_with_clusters, centroids_df])\n",
        "\n",
        "# Display df_clus_with_centroids DataFrame with centroids included\n",
        "print(df_clus_with_centroids)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RrQTdoaVGesy",
        "outputId": "b1c30a3c-3644-407a-e42d-8bfc06e16416"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    customer_longitude  customer_latitude  cluster\n",
            "0           -89.331811          43.066362        2\n",
            "1          -117.318668          33.144374        8\n",
            "2           -94.510106          37.019306        3\n",
            "3           -82.382191          36.295325        6\n",
            "4           -76.865842          39.136438        0\n",
            "..                 ...                ...      ...\n",
            "5          -122.022538          37.950805        5\n",
            "6           -83.940889          34.088114        6\n",
            "7           -81.429663          28.621742        7\n",
            "8          -117.406469          33.646703        8\n",
            "9          -122.291807          47.647486        9\n",
            "\n",
            "[3946 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert centers array into a dictionary\n",
        "centroids_dict = {cluster_index: (longitude, latitude) for cluster_index, (longitude, latitude) in enumerate(centers)}\n",
        "\n",
        "# Print the centroids dictionary\n",
        "print(centroids_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2xaKqEmeHo2d",
        "outputId": "f53c8705-c3fc-4f96-f8a1-04eace51e8ca"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: (-74.18681728, 41.08273826), 1: (-83.11412784, 42.30287408), 2: (-88.08361676999999, 42.192812535), 3: (-96.7248217, 36.88642593), 4: (-108.2974721, 41.187802695), 5: (-122.0225384, 37.95080475), 6: (-83.9408895, 34.08811401), 7: (-81.42966292, 28.6217422), 8: (-117.4064691, 33.64670253), 9: (-122.2918066, 47.64748578)}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Add centroid longitude and latitude columns to the DataFrame\n",
        "df_clus_with_centroids['centroid_longitude'] = df_clus_with_centroids['cluster'].map(lambda cluster: centroids_dict[cluster][0])\n",
        "df_clus_with_centroids['centroid_latitude'] = df_clus_with_centroids['cluster'].map(lambda cluster: centroids_dict[cluster][1])\n",
        "df_clus_with_centroids['dc_longitude'] = domestic_df ['dc_longitude']\n",
        "df_clus_with_centroids['dc_latitude'] = domestic_df ['dc_latitude']\n",
        "\n",
        "# Display the DataFrame with centroid information\n",
        "df_clus_with_centroids.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6lwGtuHVJjAp",
        "outputId": "ca64cbd9-7c6b-4f18-a822-3940b5e08204"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customer_longitude  customer_latitude  cluster  centroid_longitude  \\\n",
              "0          -89.331811          43.066362        2          -88.083617   \n",
              "1         -117.318668          33.144374        8         -117.406469   \n",
              "2          -94.510106          37.019306        3          -96.724822   \n",
              "3          -82.382191          36.295325        6          -83.940889   \n",
              "4          -76.865842          39.136438        0          -74.186817   \n",
              "\n",
              "   centroid_latitude  dc_longitude  dc_latitude  \n",
              "0          42.192813      -88.0431      30.6944  \n",
              "1          33.646703      -88.0431      30.6944  \n",
              "2          36.886426      -88.0431      30.6944  \n",
              "3          34.088114      -88.0431      30.6944  \n",
              "4          41.082738      -88.0431      30.6944  "
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_longitude</th>\n",
              "      <th>customer_latitude</th>\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        document.querySelector('#df-369864be-6e04-4e35-94f5-236bbb148b7c 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-369864be-6e04-4e35-94f5-236bbb148b7c');\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",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "\n",
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              "    display: none;\n",
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              "    height: 32px;\n",
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              "    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",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_clus_with_centroids",
              "summary": "{\n  \"name\": \"df_clus_with_centroids\",\n  \"rows\": 3946,\n  \"fields\": [\n    {\n      \"column\": \"customer_longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.953585166940062,\n        \"min\": -158.0411899,\n        \"max\": -70.99304462,\n        \"num_unique_values\": 1945,\n        \"samples\": [\n          -121.9708706,\n          -120.7712668,\n          -105.0343948\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.317642900702957,\n        \"min\": 19.43480369,\n        \"max\": 64.7485059,\n        \"num_unique_values\": 1945,\n        \"samples\": [\n          47.41037314,\n          35.66388499,\n          39.69480843\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1,\n          8,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"centroid_longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.427515139082498,\n        \"min\": -122.2918066,\n        \"max\": -74.18681728,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -83.11412784,\n          -117.4064691,\n          -122.0225384\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"centroid_latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.572738238423796,\n        \"min\": 28.6217422,\n        \"max\": 47.64748578,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          42.30287408,\n          33.64670253,\n          37.95080475\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dc_longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.89260925022127,\n        \"min\": -118.25,\n        \"max\": -73.7834,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -75.1667,\n          -87.6847,\n          -79.9333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dc_latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.421828993168068,\n        \"min\": 29.7604,\n        \"max\": 41.8369,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          39.95,\n          41.8369,\n          32.7833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to calculate Haversine distance\n",
        "def haversine_distance(lon1, lat1, lon2, lat2):\n",
        "    R = 6371.0  # Earth radius in kilometers\n",
        "    lon1_rad = radians(lon1)\n",
        "    lat1_rad = radians(lat1)\n",
        "    lon2_rad = radians(lon2)\n",
        "    lat2_rad = radians(lat2)\n",
        "\n",
        "    dlon = lon2_rad - lon1_rad\n",
        "    dlat = lat2_rad - lat1_rad\n",
        "\n",
        "    a = sin(dlat / 2)**2 + cos(lat1_rad) * cos(lat2_rad) * sin(dlon / 2)**2\n",
        "    c = 2 * atan2(sqrt(a), sqrt(1 - a))\n",
        "\n",
        "    distance = R * c\n",
        "    return distance\n",
        "\n",
        "# Calculate distances and add columns to DataFrame\n",
        "df_clus_with_centroids['customer_to_centroid'] = df_clus_with_centroids.apply(lambda row: haversine_distance(row['customer_longitude'], row['customer_latitude'], row['centroid_longitude'], row['centroid_latitude']), axis=1)\n",
        "df_clus_with_centroids['customer_to_dc'] = df_clus_with_centroids.apply(lambda row: haversine_distance(row['customer_longitude'], row['customer_latitude'], row['dc_longitude'], row['dc_latitude']), axis=1)\n",
        "\n",
        "# Display the updated DataFrame\n",
        "df_clus_with_centroids.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FiyTh4YCJEpF",
        "outputId": "bd1c6c6b-1886-42de-b3e7-1d96f9a12276"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customer_longitude  customer_latitude  cluster  centroid_longitude  \\\n",
              "0          -89.331811          43.066362        2          -88.083617   \n",
              "1         -117.318668          33.144374        8         -117.406469   \n",
              "2          -94.510106          37.019306        3          -96.724822   \n",
              "3          -82.382191          36.295325        6          -83.940889   \n",
              "4          -76.865842          39.136438        0          -74.186817   \n",
              "\n",
              "   centroid_latitude  dc_longitude  dc_latitude  customer_to_centroid  \\\n",
              "0          42.192813      -88.0431      30.6944            140.932384   \n",
              "1          33.646703      -88.0431      30.6944             56.447987   \n",
              "2          36.886426      -88.0431      30.6944            197.347306   \n",
              "3          34.088114      -88.0431      30.6944            283.358332   \n",
              "4          41.082738      -88.0431      30.6944            314.206234   \n",
              "\n",
              "   customer_to_dc  \n",
              "0     1380.416354  \n",
              "1     2767.497919  \n",
              "2      922.095816  \n",
              "3      814.156506  \n",
              "4     1383.512766  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1c527da9-f5de-4289-a155-06ce79f607a8\" 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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_longitude</th>\n",
              "      <th>customer_latitude</th>\n",
              "      <th>cluster</th>\n",
              "      <th>centroid_longitude</th>\n",
              "      <th>centroid_latitude</th>\n",
              "      <th>dc_longitude</th>\n",
              "      <th>dc_latitude</th>\n",
              "      <th>customer_to_centroid</th>\n",
              "      <th>customer_to_dc</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-89.331811</td>\n",
              "      <td>43.066362</td>\n",
              "      <td>2</td>\n",
              "      <td>-88.083617</td>\n",
              "      <td>42.192813</td>\n",
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              "      <td>140.932384</td>\n",
              "      <td>1380.416354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-117.318668</td>\n",
              "      <td>33.144374</td>\n",
              "      <td>8</td>\n",
              "      <td>-117.406469</td>\n",
              "      <td>33.646703</td>\n",
              "      <td>-88.0431</td>\n",
              "      <td>30.6944</td>\n",
              "      <td>56.447987</td>\n",
              "      <td>2767.497919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-94.510106</td>\n",
              "      <td>37.019306</td>\n",
              "      <td>3</td>\n",
              "      <td>-96.724822</td>\n",
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              "      <td>197.347306</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-82.382191</td>\n",
              "      <td>36.295325</td>\n",
              "      <td>6</td>\n",
              "      <td>-83.940889</td>\n",
              "      <td>34.088114</td>\n",
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              "      <td>30.6944</td>\n",
              "      <td>283.358332</td>\n",
              "      <td>814.156506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-76.865842</td>\n",
              "      <td>39.136438</td>\n",
              "      <td>0</td>\n",
              "      <td>-74.186817</td>\n",
              "      <td>41.082738</td>\n",
              "      <td>-88.0431</td>\n",
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              "      <td>314.206234</td>\n",
              "      <td>1383.512766</td>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "\n",
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              "    background-color: var(--bg-color);\n",
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              "  }\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",
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              "    60% {\n",
              "      border-color: transparent;\n",
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              "    }\n",
              "    80% {\n",
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              "    }\n",
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              "      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-197fa9cd-3582-44fa-8f37-cbfc8341282c 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": "df_clus_with_centroids",
              "summary": "{\n  \"name\": \"df_clus_with_centroids\",\n  \"rows\": 3946,\n  \"fields\": [\n    {\n      \"column\": \"customer_longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.953585166940062,\n        \"min\": -158.0411899,\n        \"max\": -70.99304462,\n        \"num_unique_values\": 1945,\n        \"samples\": [\n          -121.9708706,\n          -120.7712668,\n          -105.0343948\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.317642900702957,\n        \"min\": 19.43480369,\n        \"max\": 64.7485059,\n        \"num_unique_values\": 1945,\n        \"samples\": [\n          47.41037314,\n          35.66388499,\n          39.69480843\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1,\n          8,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"centroid_longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.427515139082498,\n        \"min\": -122.2918066,\n        \"max\": -74.18681728,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -83.11412784,\n          -117.4064691,\n          -122.0225384\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"centroid_latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.572738238423796,\n        \"min\": 28.6217422,\n        \"max\": 47.64748578,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          42.30287408,\n          33.64670253,\n          37.95080475\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dc_longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.89260925022127,\n        \"min\": -118.25,\n        \"max\": -73.7834,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -75.1667,\n          -87.6847,\n          -79.9333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dc_latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.421828993168068,\n        \"min\": 29.7604,\n        \"max\": 41.8369,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          39.95,\n          41.8369,\n          32.7833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_to_centroid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 385.80200387386725,\n        \"min\": 0.0,\n        \"max\": 3904.51686098632,\n        \"num_unique_values\": 1936,\n        \"samples\": [\n          432.2694493732835,\n          319.94421595579786,\n          537.6041082198661\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_to_dc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1177.3231889851904,\n        \"min\": 3.0307455749127725,\n        \"max\": 8000.422912223412,\n        \"num_unique_values\": 3604,\n        \"samples\": [\n          1679.8984736925952,\n          1550.4901874371067,\n          1330.2167895907426\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from math import radians\n",
        "\n",
        "def manhattan_distance(lon1, lat1, lon2, lat2):\n",
        "    # Earth radius in kilometers\n",
        "    R = 6371.0\n",
        "\n",
        "    # Convert decimal degrees to radians\n",
        "    lon1_rad = radians(lon1)\n",
        "    lat1_rad = radians(lat1)\n",
        "    lon2_rad = radians(lon2)\n",
        "    lat2_rad = radians(lat2)\n",
        "\n",
        "    # Calculate absolute differences\n",
        "    d_lon = abs(lon2_rad - lon1_rad)\n",
        "    d_lat = abs(lat2_rad - lat1_rad)\n",
        "\n",
        "    # Calculate Manhattan distance\n",
        "    distance = R * (d_lon + d_lat)\n",
        "\n",
        "    return distance\n",
        "\n",
        "# Calculate distances and add columns to DataFrame using Manhattan distance\n",
        "df_clus_with_centroids['customer_to_centroid_manhattan'] = df_clus_with_centroids.apply(\n",
        "    lambda row: manhattan_distance(row['customer_longitude'], row['customer_latitude'], row['centroid_longitude'], row['centroid_latitude']), axis=1)\n",
        "\n",
        "df_clus_with_centroids['customer_to_dc_manhattan'] = df_clus_with_centroids.apply(\n",
        "    lambda row: manhattan_distance(row['customer_longitude'], row['customer_latitude'], row['dc_longitude'], row['dc_latitude']), axis=1)\n",
        "\n",
        "# Display the updated DataFrame\n",
        "df_clus_with_centroids.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "Eu_UgRa9pnr-",
        "outputId": "b7311fed-101d-4f5f-cb44-104cf4f66252"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   customer_longitude  customer_latitude  cluster  centroid_longitude  \\\n",
              "0          -89.331811          43.066362        2          -88.083617   \n",
              "1         -117.318668          33.144374        8         -117.406469   \n",
              "2          -94.510106          37.019306        3          -96.724822   \n",
              "3          -82.382191          36.295325        6          -83.940889   \n",
              "4          -76.865842          39.136438        0          -74.186817   \n",
              "\n",
              "   centroid_latitude  dc_longitude  dc_latitude  customer_to_centroid  \\\n",
              "0          42.192813      -88.0431      30.6944            140.932384   \n",
              "1          33.646703      -88.0431      30.6944             56.447987   \n",
              "2          36.886426      -88.0431      30.6944            197.347306   \n",
              "3          34.088114      -88.0431      30.6944            283.358332   \n",
              "4          41.082738      -88.0431      30.6944            314.206234   \n",
              "\n",
              "   customer_to_dc  customer_to_centroid_manhattan  customer_to_dc_manhattan  \n",
              "0     1380.416354                      235.927119               1518.997517  \n",
              "1     2767.497919                       65.619388               3527.719349  \n",
              "2      922.095816                      261.040742               1422.395730  \n",
              "3      814.156506                      418.750080               1252.258856  \n",
              "4     1383.512766                      514.312675               2181.566176  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\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>customer_longitude</th>\n",
              "      <th>customer_latitude</th>\n",
              "      <th>cluster</th>\n",
              "      <th>centroid_longitude</th>\n",
              "      <th>centroid_latitude</th>\n",
              "      <th>dc_longitude</th>\n",
              "      <th>dc_latitude</th>\n",
              "      <th>customer_to_centroid</th>\n",
              "      <th>customer_to_dc</th>\n",
              "      <th>customer_to_centroid_manhattan</th>\n",
              "      <th>customer_to_dc_manhattan</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-89.331811</td>\n",
              "      <td>43.066362</td>\n",
              "      <td>2</td>\n",
              "      <td>-88.083617</td>\n",
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              "      <td>235.927119</td>\n",
              "      <td>1518.997517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-117.318668</td>\n",
              "      <td>33.144374</td>\n",
              "      <td>8</td>\n",
              "      <td>-117.406469</td>\n",
              "      <td>33.646703</td>\n",
              "      <td>-88.0431</td>\n",
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              "      <td>3527.719349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-94.510106</td>\n",
              "      <td>37.019306</td>\n",
              "      <td>3</td>\n",
              "      <td>-96.724822</td>\n",
              "      <td>36.886426</td>\n",
              "      <td>-88.0431</td>\n",
              "      <td>30.6944</td>\n",
              "      <td>197.347306</td>\n",
              "      <td>922.095816</td>\n",
              "      <td>261.040742</td>\n",
              "      <td>1422.395730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-82.382191</td>\n",
              "      <td>36.295325</td>\n",
              "      <td>6</td>\n",
              "      <td>-83.940889</td>\n",
              "      <td>34.088114</td>\n",
              "      <td>-88.0431</td>\n",
              "      <td>30.6944</td>\n",
              "      <td>283.358332</td>\n",
              "      <td>814.156506</td>\n",
              "      <td>418.750080</td>\n",
              "      <td>1252.258856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-76.865842</td>\n",
              "      <td>39.136438</td>\n",
              "      <td>0</td>\n",
              "      <td>-74.186817</td>\n",
              "      <td>41.082738</td>\n",
              "      <td>-88.0431</td>\n",
              "      <td>30.6944</td>\n",
              "      <td>314.206234</td>\n",
              "      <td>1383.512766</td>\n",
              "      <td>514.312675</td>\n",
              "      <td>2181.566176</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\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",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    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",
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              "    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",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      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",
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              "\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-34123c48-99aa-4277-b0d7-a89b34a77246 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": "df_clus_with_centroids",
              "summary": "{\n  \"name\": \"df_clus_with_centroids\",\n  \"rows\": 3946,\n  \"fields\": [\n    {\n      \"column\": \"customer_longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.953585166940062,\n        \"min\": -158.0411899,\n        \"max\": -70.99304462,\n        \"num_unique_values\": 1945,\n        \"samples\": [\n          -121.9708706,\n          -120.7712668,\n          -105.0343948\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.317642900702957,\n        \"min\": 19.43480369,\n        \"max\": 64.7485059,\n        \"num_unique_values\": 1945,\n        \"samples\": [\n          47.41037314,\n          35.66388499,\n          39.69480843\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cluster\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1,\n          8,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"centroid_longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.427515139082498,\n        \"min\": -122.2918066,\n        \"max\": -74.18681728,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -83.11412784,\n          -117.4064691,\n          -122.0225384\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"centroid_latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.572738238423796,\n        \"min\": 28.6217422,\n        \"max\": 47.64748578,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          42.30287408,\n          33.64670253,\n          37.95080475\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dc_longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.89260925022127,\n        \"min\": -118.25,\n        \"max\": -73.7834,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -75.1667,\n          -87.6847,\n          -79.9333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dc_latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.421828993168068,\n        \"min\": 29.7604,\n        \"max\": 41.8369,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          39.95,\n          41.8369,\n          32.7833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_to_centroid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 385.80200387386725,\n        \"min\": 0.0,\n        \"max\": 3904.51686098632,\n        \"num_unique_values\": 1936,\n        \"samples\": [\n          432.2694493732835,\n          319.94421595579786,\n          537.6041082198661\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_to_dc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1177.3231889851904,\n        \"min\": 3.0307455749127725,\n        \"max\": 8000.422912223412,\n        \"num_unique_values\": 3604,\n        \"samples\": [\n          1679.8984736925952,\n          1550.4901874371067,\n          1330.2167895907426\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_to_centroid_manhattan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 573.0945383292401,\n        \"min\": 0.0,\n        \"max\": 5849.966358392401,\n        \"num_unique_values\": 1936,\n        \"samples\": [\n          445.10289796837515,\n          497.7856431489491,\n          916.5167011171371\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"customer_to_dc_manhattan\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1700.9488602512322,\n        \"min\": 4.9471901605797,\n        \"max\": 11495.910964396719,\n        \"num_unique_values\": 3604,\n        \"samples\": [\n          2280.7796960516457,\n          2121.569810447118,\n          2051.6942914042916\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# euclidean distance\n",
        "print('centroids distance: ', round(df_clus_with_centroids['customer_to_centroid'].sum(), 2))\n",
        "print('current dc distance: ', round(df_clus_with_centroids['customer_to_dc'].sum(), 2))\n",
        "print('save # km: ', round(df_clus_with_centroids['customer_to_dc'].sum(), 2) - round(df_clus_with_centroids['customer_to_centroid'].sum(), 2))\n",
        "print('percentage reduce: ', round((((df_clus_with_centroids['customer_to_dc'].sum() - df_clus_with_centroids['customer_to_centroid'].sum()) / df_clus_with_centroids['customer_to_dc'].sum()) * 100), 2), '%')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FIdOcw1RsFeJ",
        "outputId": "9a4caa34-194f-4bff-ab8c-9f421b3fb08e"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "centroids distance:  1194912.93\n",
            "current dc distance:  7090704.85\n",
            "save # km:  5895791.92\n",
            "percentage reduce:  83.15 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# manhattan distance\n",
        "print('centroids distance: ', round(df_clus_with_centroids['customer_to_centroid_manhattan'].sum(), 2))\n",
        "print('current dc distance: ', round(df_clus_with_centroids['customer_to_dc_manhattan'].sum(), 2))\n",
        "print('save # km: ', round(df_clus_with_centroids['customer_to_dc_manhattan'].sum(), 2) - round(df_clus_with_centroids['customer_to_centroid_manhattan'].sum(), 2))\n",
        "print('percentage reduce: ', round((((df_clus_with_centroids['customer_to_dc_manhattan'].sum() - df_clus_with_centroids['customer_to_centroid_manhattan'].sum()) / df_clus_with_centroids['customer_to_dc_manhattan'].sum()) * 100), 2), '%')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iYBfwIzOMV96",
        "outputId": "c29ac4ef-9706-4178-d112-8129df8ec14e"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "centroids distance:  1688942.69\n",
            "current dc distance:  10185974.77\n",
            "save # km:  8497032.08\n",
            "percentage reduce:  83.42 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://axk51013.medium.com/%E4%B8%8D%E8%A6%81%E5%86%8D%E7%94%A8k-means-%E8%B6%85%E5%AF%A6%E7%94%A8%E5%88%86%E7%BE%A4%E6%B3%95dbscan%E8%A9%B3%E8%A7%A3-a33fa287c0e"
      ],
      "metadata": {
        "id": "ekGFY5_eLh9F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3. Time-series analysis (2019-01~2024-03)"
      ],
      "metadata": {
        "id": "2V8VJLfXxTOq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install prophet\n",
        "from prophet import Prophet  # Import Prophet library\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from prophet import Prophet  # Assuming you have installed the Prophet package"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DdjzwbPwTZpa",
        "outputId": "92d8429c-c82b-4adf-e66f-270d9b3dd7a2"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: prophet in /usr/local/lib/python3.10/dist-packages (1.1.5)\n",
            "Requirement already satisfied: cmdstanpy>=1.0.4 in /usr/local/lib/python3.10/dist-packages (from prophet) (1.2.1)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.10/dist-packages (from prophet) (1.25.2)\n",
            "Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from prophet) (3.7.1)\n",
            "Requirement already satisfied: pandas>=1.0.4 in /usr/local/lib/python3.10/dist-packages (from prophet) (1.5.3)\n",
            "Requirement already satisfied: holidays>=0.25 in /usr/local/lib/python3.10/dist-packages (from prophet) (0.45)\n",
            "Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.10/dist-packages (from prophet) (4.66.2)\n",
            "Requirement already satisfied: importlib-resources in /usr/local/lib/python3.10/dist-packages (from prophet) (6.3.2)\n",
            "Requirement already satisfied: stanio~=0.3.0 in /usr/local/lib/python3.10/dist-packages (from cmdstanpy>=1.0.4->prophet) (0.3.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from holidays>=0.25->prophet) (2.8.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (4.50.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (24.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (9.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.0.0->prophet) (3.1.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.0.4->prophet) (2023.4)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil->holidays>=0.25->prophet) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import data\n",
        "sales_trend_2019_2024_data ='https://raw.githubusercontent.com/Taweilo/theLook_ecommerce/main/Data/sales_trend_2019_2024_data.csv'\n",
        "ts_df = pd.read_csv(sales_trend_2019_2024_data)\n",
        "ts_df = ts_df.sort_values(['year','month'])\n",
        "ts_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "dBoF10jBxYSN",
        "outputId": "58cd1539-a8aa-4022-ac6b-3c4f71ba1598"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    year  month time_period  total_revenue  total_cost  gross_profit\n",
              "62  2019      1      2019-1         957.43      450.43        507.00\n",
              "61  2019      2      2019-2        3240.24     1551.79       1688.45\n",
              "60  2019      3      2019-3        5511.08     2710.77       2800.31\n",
              "59  2019      4      2019-4        6961.44     3361.76       3599.68\n",
              "58  2019      5      2019-5       10263.49     4751.46       5512.03\n",
              "..   ...    ...         ...            ...         ...           ...\n",
              "4   2023     11     2023-11      344636.13   165599.26     179036.87\n",
              "3   2023     12     2023-12      383973.78   183923.09     200050.69\n",
              "2   2024      1      2024-1      432219.21   208032.96     224186.25\n",
              "1   2024      2      2024-2      473310.36   227411.39     245898.97\n",
              "0   2024      3      2024-3      731720.96   352032.40     379688.56\n",
              "\n",
              "[63 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1f8a6193-8670-43e5-a119-a19ac974bea6\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "\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>year</th>\n",
              "      <th>month</th>\n",
              "      <th>time_period</th>\n",
              "      <th>total_revenue</th>\n",
              "      <th>total_cost</th>\n",
              "      <th>gross_profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>2019</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-1</td>\n",
              "      <td>957.43</td>\n",
              "      <td>450.43</td>\n",
              "      <td>507.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>2019</td>\n",
              "      <td>2</td>\n",
              "      <td>2019-2</td>\n",
              "      <td>3240.24</td>\n",
              "      <td>1551.79</td>\n",
              "      <td>1688.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>2019</td>\n",
              "      <td>3</td>\n",
              "      <td>2019-3</td>\n",
              "      <td>5511.08</td>\n",
              "      <td>2710.77</td>\n",
              "      <td>2800.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>2019</td>\n",
              "      <td>4</td>\n",
              "      <td>2019-4</td>\n",
              "      <td>6961.44</td>\n",
              "      <td>3361.76</td>\n",
              "      <td>3599.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>2019</td>\n",
              "      <td>5</td>\n",
              "      <td>2019-5</td>\n",
              "      <td>10263.49</td>\n",
              "      <td>4751.46</td>\n",
              "      <td>5512.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023</td>\n",
              "      <td>11</td>\n",
              "      <td>2023-11</td>\n",
              "      <td>344636.13</td>\n",
              "      <td>165599.26</td>\n",
              "      <td>179036.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023</td>\n",
              "      <td>12</td>\n",
              "      <td>2023-12</td>\n",
              "      <td>383973.78</td>\n",
              "      <td>183923.09</td>\n",
              "      <td>200050.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2024</td>\n",
              "      <td>1</td>\n",
              "      <td>2024-1</td>\n",
              "      <td>432219.21</td>\n",
              "      <td>208032.96</td>\n",
              "      <td>224186.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2024</td>\n",
              "      <td>2</td>\n",
              "      <td>2024-2</td>\n",
              "      <td>473310.36</td>\n",
              "      <td>227411.39</td>\n",
              "      <td>245898.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2024</td>\n",
              "      <td>3</td>\n",
              "      <td>2024-3</td>\n",
              "      <td>731720.96</td>\n",
              "      <td>352032.40</td>\n",
              "      <td>379688.56</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>63 rows × 6 columns</p>\n",
              "</div>\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f8a6193-8670-43e5-a119-a19ac974bea6')\"\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-1f8a6193-8670-43e5-a119-a19ac974bea6 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-1f8a6193-8670-43e5-a119-a19ac974bea6');\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-31ff641b-6864-46e2-847c-d26a061d6b65\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-31ff641b-6864-46e2-847c-d26a061d6b65')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\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",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\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-31ff641b-6864-46e2-847c-d26a061d6b65 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_77d1f15c-6c7e-4afb-8a4f-b261ee3df553\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-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('ts_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",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_77d1f15c-6c7e-4afb-8a4f-b261ee3df553 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('ts_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ts_df",
              "summary": "{\n  \"name\": \"ts_df\",\n  \"rows\": 63,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2019,\n        \"max\": 2024,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2019,\n          2020,\n          2024\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          11,\n          10,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_period\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 63,\n        \"samples\": [\n          \"2024-2\",\n          \"2023-10\",\n          \"2019-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_revenue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 134298.2114659353,\n        \"min\": 957.43,\n        \"max\": 731720.96,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          473310.36,\n          314314.57,\n          957.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_cost\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64562.79852038363,\n        \"min\": 450.43,\n        \"max\": 352032.4,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          227411.39,\n          151861.5,\n          450.43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"gross_profit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 69736.58131461243,\n        \"min\": 507.0,\n        \"max\": 379688.56,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          245898.97,\n          162453.07,\n          507.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the trend\n",
        "plt.figure(figsize=(12, 10))\n",
        "plt.plot(ts_df['time_period'], ts_df['total_revenue'], marker='o')\n",
        "plt.xlabel('Time Period')\n",
        "plt.ylabel('Total Revenue')\n",
        "plt.title('Sales Trend from 2019 to 2024')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 909
        },
        "id": "WFe96-h8-FGi",
        "outputId": "1d04aa2e-fb15-4179-e101-9e4800f9ae3f"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prepare the data for Prophet\n",
        "prophet_df = ts_df[['time_period', 'total_revenue']]\n",
        "prophet_df.columns = ['ds', 'y']  # Rename columns for Prophet\n",
        "\n",
        "# Initialize Prophet model\n",
        "model = Prophet(seasonality_mode='additive', holidays_prior_scale=0, yearly_seasonality=False)  # Set seasonality_mode and disable holiday seasonality\n",
        "\n",
        "# Fit the model\n",
        "model.fit(prophet_df)\n",
        "\n",
        "# Make future predictions\n",
        "future = model.make_future_dataframe(periods=12, freq='M')  # Predicting 12 months into the future\n",
        "forecast = model.predict(future)\n",
        "\n",
        "# Calculate Mean Absolute Error (MAE)\n",
        "actual_values = prophet_df['y'].values\n",
        "predicted_values = forecast['yhat'].values[:-12]  # Exclude the forecasted future values\n",
        "mape = np.mean(np.abs((actual_values - predicted_values) / actual_values)) * 100\n",
        "print('MAPE: %.3f%%' % mape)\n",
        "\n",
        "# Plot the forecast\n",
        "fig = model.plot(forecast)\n",
        "plt.xlabel('Time Period')\n",
        "plt.ylabel('Total Revenue')\n",
        "plt.title('Revenue Forecast with Prophet (Smooth Line, No Holiday Seasonality)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 866
        },
        "id": "URWCNFLi-FUp",
        "outputId": "5acc064c-4ec2-4794-bb56-9b5602a10bc2"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpey30ragn/4mwgokmu.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpey30ragn/fktuvtsw.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=6270', 'data', 'file=/tmp/tmpey30ragn/4mwgokmu.json', 'init=/tmp/tmpey30ragn/fktuvtsw.json', 'output', 'file=/tmp/tmpey30ragn/prophet_model0_sm_kx4/prophet_model-20240327023407.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']\n",
            "02:34:07 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "02:34:07 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAPE: 50.929%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://machinelearningmastery.com/time-series-forecasting-with-prophet-in-python/\n"
      ],
      "metadata": {
        "id": "mKR7gTMOaED-"
      }
    }
  ]
}