{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ba6aba8"
      },
      "source": [
        "# 🤖 **Data Collection, Creation, Storage, and Processing**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jpASMyIQMaAq"
      },
      "source": [
        "## **1.** Title cell (Markdown)\n",
        "\n",
        "# Prime Video Revenue Estimation\n",
        "\n",
        "Goal: Estimate annual revenues based on number of monthly subscribers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lquNYCbfL9IM"
      },
      "source": [
        "## **2.** Import libraries (Code cell)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "FDhule9EPi7U"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-1Pr2szaqLk"
      },
      "source": [
        "## **3.** Create dataset (Code cell)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = {\n",
        "    \"Year\": [2019, 2020, 2021, 2022, 2023],\n",
        "    \"Subscribers_millions\": [120, 150, 175, 200, 220]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "df"
      ],
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        "colab": {
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          "height": 206
        },
        "id": "wJY41Y2LP0F_",
        "outputId": "d9396379-f790-4e1c-9b0d-8425af101e24"
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      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year  Subscribers_millions\n",
              "0  2019                   120\n",
              "1  2020                   150\n",
              "2  2021                   175\n",
              "3  2022                   200\n",
              "4  2023                   220"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2019,\n        \"max\": 2023,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2020,\n          2023,\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subscribers_millions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39,\n        \"min\": 120,\n        \"max\": 220,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          150,\n          220,\n          175\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T8AdKkmASq9a"
      },
      "source": [
        "## **4.** Explanation of formula (Markdown)\n",
        "\n",
        "## Revenue Formula\n",
        "\n",
        "Annual Revenue = Subscribers × Monthly Price × 12\n",
        "\n",
        "We assume a fixed monthly price for Prime Video."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7g9gqBgQMtJn"
      },
      "source": [
        "## **5.** Define price (Code cell)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "monthly_price = 8.99"
      ],
      "metadata": {
        "id": "O-UYI2D2QZeB"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **6.** Revenue calculation (Code cell)"
      ],
      "metadata": {
        "id": "zngJe7meQhZu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Annual_Revenue_Billion_$\"] = df[\"Subscribers_millions\"] * monthly_price * 12 / 1000\n",
        "df"
      ],
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        "id": "5Ybe_yVdQtd6",
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      "execution_count": 24,
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          "data": {
            "text/plain": [
              "   Year  Subscribers_millions  Annual_Revenue_Billion_$\n",
              "0  2019                   120                   12.9456\n",
              "1  2020                   150                   16.1820\n",
              "2  2021                   175                   18.8790\n",
              "3  2022                   200                   21.5760\n",
              "4  2023                   220                   23.7336"
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              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9029914f-0d4b-4d7c-87d7-88f890049b07 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2019,\n        \"max\": 2023,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2020,\n          2023,\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Subscribers_millions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39,\n        \"min\": 120,\n        \"max\": 220,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          150,\n          220,\n          175\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual_Revenue_Billion_$\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.274553568268855,\n        \"min\": 12.945599999999999,\n        \"max\": 23.7336,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          16.182,\n          23.7336,\n          18.879\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7.  Visualization (Code cell)"
      ],
      "metadata": {
        "id": "EjJEqlE9Q5Sw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df[\"Year\"], df[\"Annual_Revenue_Billion_$\"], marker='o')\n",
        "plt.title(\"Estimated Prime Video Annual Revenue\")\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"Revenue (Billion $)\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "UaiDhNLdRJIZ",
        "outputId": "2ed43b8f-c40c-48ab-c6be-cd9423470f0e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 8. Interpretation (Markdown)\n",
        "\n",
        "## Interpretation\n",
        "\n",
        "The estimated revenue increases over time due to the growth in subscribers.\n",
        "\n",
        "However, this model has limitations:\n",
        "- It assumes a constant subscription price\n",
        "- It does not account for Amazon Prime bundling\n",
        "- Regional price differences are ignored\n",
        "\n",
        "Therefore, the results are an approximation."
      ],
      "metadata": {
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      }
    },
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      "cell_type": "markdown",
      "source": [
        "##"
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