notebooks/AB_Test_Experiment_Evaluation_Results.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iNXzg2PoROmo"
      },
      "source": [
        "# A/B Testing\n",
        "\n",
        "Also known as **split testing or bucket testing**, used for comparing two versions of features (typically, a webpage or app feature) against each other to determine which one performs better, ensuring data-driven decision-making for the feature implementation on the production.\n",
        "\n",
        "###  Problem Statement:\n",
        "Evaluate the impact of adding 360-degree product videos feature on the product page on sales for an E-Commerce Platform.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1jsaKRTWlTw"
      },
      "source": [
        "![Screenshot 2024-06-17 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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kRyXAwzFWd_a"
      },
      "source": [
        "The goal is to determine whether including 360-degree product videos increases the conversion rate and overall revenue compared to a product page without these videos and only images of the products.\n",
        "\n",
        "###  Dataset Column Names:\n",
        "* **UserID**: Unique identifier for each user.\n",
        "* **Group**: A/B test group identifier ('Control' for no video(only product images), 'Treatment' for 360-degree video with product images).\n",
        "* **Timestamp**: The time when the user interacted with the system.\n",
        "* **PageViewed**: The specific page or feature the user interacted with (e.g., 'Home Page', 'Product Page', 'Checkout Page').\n",
        "* **Action**: The action taken by the user (e.g., 'View Product', 'Add to Cart', 'Purchase').\n",
        "* **Conversion**: A binary indicator (0 for no purchase, 1 for purchase).\n",
        "* **Revenue**: The amount of money spent by the user (if applicable).\n",
        "* **DeviceType**: Type of device used (e.g., 'Desktop', 'Mobile').\n",
        "* **SessionDuration**: The duration of the user session in seconds.\n",
        "* **Location**: Geographic location of the user."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Project Process:\n",
        "\n",
        "The project utilized Python, with the following steps:\n",
        "\n",
        "1. **Data Preprocessing**: The data was initially divided into two groups: control and treatment. Also performed test to ensured that no overlapping users, confirming unique users in each dataset.\n",
        "2. **Conversion Rate Calculation**: Calculated the conversion rates for both the control and treatment groups by determining the percentage of users who completed a purchase.\n",
        "3. **Revenue Analysis**: Calculated the **Average Revenue Per User (ARPU)** for both the control and treatment groups to assess the financial impact of the 360-degree product video feature.\n",
        "4. **User Engagement**: Measured **user engagement** by comparing the improvement in conversion rates between the treatment and control groups.\n",
        "5. **Time Series Analysis**: Analyzed **conversion rates** over time to determine how long the campaign needed to run to achieve the target results.\n",
        "6. **Statistical Testing**: Conducted a **Chi-Square Test** to assess if the difference in conversion rates between the control and treatment groups was statistically significant.\n",
        "7. **Data Visualization**: Visualized conversion rates, revenue, and statistical test results using **bar plots** and **mosaic plots** for easier interpretation of results.\n",
        "\n",
        "\n",
        "### Conclusion\n",
        "\n",
        "The A/B test demonstrated that the addition of 360-degree product videos led to a significant improvement in user engagement and revenue. The treatment group showed a 64.77% increase in user interaction and a 2.87% rise in overall revenue compared to the control group. The statistical test confirmed these results with a low p-value, indicating that the differences were statistically significant. These findings suggest that implementing 360-degree product videos will positively impact sales and customer engagement."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now, let's dive into the project!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qh44D2s98KVQ"
      },
      "source": [
        "### Building a Fake Dataset for A/B Testing\n",
        "\n",
        "I'll create two datasets: Control and Treatment Respectively, using `Faker` Library, for my specific needs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K5DCLcLwutEf",
        "outputId": "7ac39c74-4d47-4112-8919-5fbd1390065d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: Faker in /usr/local/lib/python3.10/dist-packages (25.9.1)\n",
            "Requirement already satisfied: python-dateutil>=2.4 in /usr/local/lib/python3.10/dist-packages (from Faker) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.4->Faker) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install Faker"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "zwBTtj1GuaG6"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from faker import Faker\n",
        "from datetime import datetime"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "mAXkjGXJs_TO"
      },
      "outputs": [],
      "source": [
        "# Initialize Faker and random seed for reproducibility\n",
        "faker = Faker()\n",
        "np.random.seed(42)\n",
        "\n",
        "# Constants\n",
        "num_rows = 50000\n",
        "group_choices = ['Control', 'Treatment']\n",
        "page_viewed = 'Product Page'\n",
        "device_choices = ['Desktop', 'Mobile']\n",
        "location_choices = ['New Delhi', 'Mumbai', 'Bangalore', 'Hyderabad', 'Chennai', 'Kolkata']\n",
        "\n",
        "# Generate fake data for Control group\n",
        "control_data = {\n",
        "    'UserID': [faker.uuid4() for _ in range(num_rows)],\n",
        "    'Group': ['Control'] * num_rows,\n",
        "    'Timestamp': [faker.date_time_between(start_date=datetime(2024, 1, 1), end_date=datetime(2024, 1, 31)) for _ in range(num_rows)],\n",
        "    'PageViewed': [page_viewed] * num_rows,\n",
        "    'DeviceType': np.random.choice(device_choices, num_rows),\n",
        "    'SessionDuration': np.random.randint(1, 30, num_rows),\n",
        "    'Location': np.random.choice(location_choices, num_rows)\n",
        "}\n",
        "\n",
        "# Initialize Conversion and Revenue lists\n",
        "conversions = []\n",
        "revenues = []\n",
        "\n",
        "# Adjust conversion and revenue for control and treatment groups\n",
        "for i in range(num_rows):\n",
        "    conversion = np.random.choice([0, 1], p=[0.90, 0.1])  # 10% conversion rate for control\n",
        "    conversions.append(conversion)\n",
        "    if conversion == 1:\n",
        "        revenues.append(np.random.choice([500, 1000, 2500, 4000], p=[0.30, 0.30, 0.25, 0.15]))\n",
        "    else:\n",
        "        revenues.append(0)\n",
        "\n",
        "# Add Conversion and Revenue to data dictionary\n",
        "control_data['Revenue'] = revenues\n",
        "control_data['Conversion'] = conversions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "0LAiwFjstdkA"
      },
      "outputs": [],
      "source": [
        "# Combine the data into a single DataFrame\n",
        "df_control = pd.DataFrame(control_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "GXUmrCVgt2uI",
        "outputId": "b6c8b8a5-9685-460e-9190-75c4973a69ec"
      },
      "outputs": [
        {
          "data": {
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              "type": "dataframe",
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            },
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              "    <div>\n",
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              "      <th>UserID</th>\n",
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              "      <th>Conversion</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>2024-01-24 18:11:03.698813</td>\n",
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              "      <td>Mobile</td>\n",
              "      <td>26</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>Control</td>\n",
              "      <td>2024-01-17 06:28:27.690137</td>\n",
              "      <td>Product Page</td>\n",
              "      <td>Desktop</td>\n",
              "      <td>17</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>e14d5012-552a-40f7-ba28-c5c9a6d29b43</td>\n",
              "      <td>Control</td>\n",
              "      <td>2024-01-18 04:02:23.828361</td>\n",
              "      <td>Product Page</td>\n",
              "      <td>Desktop</td>\n",
              "      <td>19</td>\n",
              "      <td>Chennai</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6a83d945-5f6d-4499-b2b4-28e581a5d3b6</td>\n",
              "      <td>Control</td>\n",
              "      <td>2024-01-21 07:34:20.947844</td>\n",
              "      <td>Product Page</td>\n",
              "      <td>Desktop</td>\n",
              "      <td>12</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49995</th>\n",
              "      <td>ca6a2bb1-110b-43ba-a1ab-f4bd3079d383</td>\n",
              "      <td>Control</td>\n",
              "      <td>2024-01-23 01:32:38.580367</td>\n",
              "      <td>Product Page</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>20</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49996</th>\n",
              "      <td>bf7af47f-ff65-44a1-886a-1735a3aa0688</td>\n",
              "      <td>Control</td>\n",
              "      <td>2024-01-12 09:55:50.867387</td>\n",
              "      <td>Product Page</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>4</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49997</th>\n",
              "      <td>df9ce9f9-8a22-474f-9ec7-9bde934d5736</td>\n",
              "      <td>Control</td>\n",
              "      <td>2024-01-22 09:08:24.233551</td>\n",
              "      <td>Product Page</td>\n",
              "      <td>Desktop</td>\n",
              "      <td>5</td>\n",
              "      <td>Mumbai</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49998</th>\n",
              "      <td>aa578fa1-52f9-4b1f-a055-21136b3a4210</td>\n",
              "      <td>Control</td>\n",
              "      <td>2024-01-18 06:55:13.792083</td>\n",
              "      <td>Product Page</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>14</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49999</th>\n",
              "      <td>8efbd330-3501-4b65-a9b1-0ea3f08cae76</td>\n",
              "      <td>Control</td>\n",
              "      <td>2024-01-28 04:23:18.524143</td>\n",
              "      <td>Product Page</td>\n",
              "      <td>Desktop</td>\n",
              "      <td>9</td>\n",
              "      <td>Bangalore</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>50000 rows × 9 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d4c6cd1-f4d2-4480-94e5-4d8d88cc3b46')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "\n",
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              "        document.querySelector('#df-4d4c6cd1-f4d2-4480-94e5-4d8d88cc3b46 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\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",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  }\n",
              "\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",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    }\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-45d19cd6-47e4-446d-bc8d-4ddd2e45a882 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_0e0bc0f0-4f7b-4634-882c-96defc01940d\">\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",
              "        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",
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              "\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_control')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
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            "text/plain": [
              "                                     UserID    Group  \\\n",
              "0      1ced8d89-8cfd-4631-8cee-a7c7ca7eaff1  Control   \n",
              "1      62dbb207-024b-4d6a-b93b-22b78ecf6e78  Control   \n",
              "2      76ca9a17-6bed-4a7b-bd78-44a6f5a194d2  Control   \n",
              "3      e14d5012-552a-40f7-ba28-c5c9a6d29b43  Control   \n",
              "4      6a83d945-5f6d-4499-b2b4-28e581a5d3b6  Control   \n",
              "...                                     ...      ...   \n",
              "49995  ca6a2bb1-110b-43ba-a1ab-f4bd3079d383  Control   \n",
              "49996  bf7af47f-ff65-44a1-886a-1735a3aa0688  Control   \n",
              "49997  df9ce9f9-8a22-474f-9ec7-9bde934d5736  Control   \n",
              "49998  aa578fa1-52f9-4b1f-a055-21136b3a4210  Control   \n",
              "49999  8efbd330-3501-4b65-a9b1-0ea3f08cae76  Control   \n",
              "\n",
              "                       Timestamp    PageViewed DeviceType  SessionDuration  \\\n",
              "0     2024-01-01 04:05:55.699296  Product Page    Desktop               26   \n",
              "1     2024-01-24 18:11:03.698813  Product Page     Mobile               26   \n",
              "2     2024-01-17 06:28:27.690137  Product Page    Desktop               17   \n",
              "3     2024-01-18 04:02:23.828361  Product Page    Desktop               19   \n",
              "4     2024-01-21 07:34:20.947844  Product Page    Desktop               12   \n",
              "...                          ...           ...        ...              ...   \n",
              "49995 2024-01-23 01:32:38.580367  Product Page     Mobile               20   \n",
              "49996 2024-01-12 09:55:50.867387  Product Page     Mobile                4   \n",
              "49997 2024-01-22 09:08:24.233551  Product Page    Desktop                5   \n",
              "49998 2024-01-18 06:55:13.792083  Product Page     Mobile               14   \n",
              "49999 2024-01-28 04:23:18.524143  Product Page    Desktop                9   \n",
              "\n",
              "        Location  Revenue  Conversion  \n",
              "0      New Delhi        0           0  \n",
              "1        Chennai        0           0  \n",
              "2        Chennai        0           0  \n",
              "3        Chennai        0           0  \n",
              "4         Mumbai      500           1  \n",
              "...          ...      ...         ...  \n",
              "49995     Mumbai        0           0  \n",
              "49996  New Delhi        0           0  \n",
              "49997     Mumbai        0           0  \n",
              "49998  Bangalore        0           0  \n",
              "49999  Bangalore        0           0  \n",
              "\n",
              "[50000 rows x 9 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_control"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "O0ZoiJ4yuiHV"
      },
      "outputs": [],
      "source": [
        "# Generate fake data for Control group\n",
        "treatment_data = {\n",
        "    'UserID': [faker.uuid4() for _ in range(num_rows)],\n",
        "    'Group': ['Treatment'] * num_rows,\n",
        "    'Timestamp': [faker.date_time_between(start_date=datetime(2024, 1, 1), end_date=datetime(2024, 1, 31)) for _ in range(num_rows)],\n",
        "    'PageViewed': [page_viewed] * num_rows,\n",
        "    'DeviceType': np.random.choice(device_choices, num_rows),\n",
        "    'SessionDuration': np.random.randint(1, 30, num_rows),\n",
        "    'Location': np.random.choice(location_choices, num_rows)\n",
        "}\n",
        "\n",
        "# Initialize Conversion and Revenue lists\n",
        "conversions = []\n",
        "revenues = []\n",
        "\n",
        "# Adjust conversion and revenue for control and treatment groups\n",
        "for i in range(num_rows):\n",
        "    conversion = np.random.choice([0, 1], p=[0.84, 0.16])  # 10% conversion rate for control\n",
        "    conversions.append(conversion)\n",
        "    if conversion == 1:\n",
        "        revenues.append(np.random.choice([500, 1000, 2500, 4000], p=[0.35, 0.25, 0.20, 0.20]))\n",
        "    else:\n",
        "        revenues.append(0)\n",
        "\n",
        "# Add Conversion and Revenue to data dictionary\n",
        "treatment_data['Revenue'] = revenues\n",
        "treatment_data['Conversion'] = conversions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "cW_5vR3ouiEG"
      },
      "outputs": [],
      "source": [
        "df_treatment = pd.DataFrame(treatment_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "WjX6Ncn9uiBM",
        "outputId": "b1a13495-9aca-46e6-f335-2a37741a841d"
      },
      "outputs": [
        {
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              "type": "dataframe",
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              "      <td>Product Page</td>\n",
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              "      <td>1000</td>\n",
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              "      <td>Treatment</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
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              "      <th>49995</th>\n",
              "      <td>27621109-b9af-4022-ae5c-917ac102aa6d</td>\n",
              "      <td>Treatment</td>\n",
              "      <td>2024-01-29 22:02:53.537204</td>\n",
              "      <td>Product Page</td>\n",
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              "      <td>2024-01-08 17:14:19.875358</td>\n",
              "      <td>Product Page</td>\n",
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              "      <th>49998</th>\n",
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              "      <td>2024-01-20 19:12:33.862904</td>\n",
              "      <td>Product Page</td>\n",
              "      <td>Mobile</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>50000 rows × 9 columns</p>\n",
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              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "                                     UserID      Group  \\\n",
              "0      b381075f-7ef6-4156-9d18-213456bc831b  Treatment   \n",
              "1      15165829-07c9-47bc-8d02-528c9d069c93  Treatment   \n",
              "2      53ffb2ed-6fb1-4a10-b0a8-f86c131cd253  Treatment   \n",
              "3      eca4b613-a76e-4c98-a07f-31909a2a5035  Treatment   \n",
              "4      936a2a92-4382-4ead-b842-10bd5ef6be82  Treatment   \n",
              "...                                     ...        ...   \n",
              "49995  27621109-b9af-4022-ae5c-917ac102aa6d  Treatment   \n",
              "49996  eeceed4e-b610-4d52-be5f-84b3e7a7da03  Treatment   \n",
              "49997  4a914080-e8d4-42b0-b1b1-c2803b30a05c  Treatment   \n",
              "49998  d8c36ce4-6d3a-4f13-aa2e-abd68b851ed6  Treatment   \n",
              "49999  6736b6d1-7759-48c8-8d04-879a57ccb89b  Treatment   \n",
              "\n",
              "                       Timestamp    PageViewed DeviceType  SessionDuration  \\\n",
              "0     2024-01-29 21:21:02.598575  Product Page    Desktop               13   \n",
              "1     2024-01-20 08:47:39.633623  Product Page     Mobile               20   \n",
              "2     2024-01-24 03:24:59.534347  Product Page     Mobile                4   \n",
              "3     2024-01-04 08:52:50.404678  Product Page    Desktop               24   \n",
              "4     2024-01-17 03:37:24.777533  Product Page    Desktop               22   \n",
              "...                          ...           ...        ...              ...   \n",
              "49995 2024-01-29 22:02:53.537204  Product Page    Desktop               25   \n",
              "49996 2024-01-08 17:14:19.875358  Product Page     Mobile               22   \n",
              "49997 2024-01-17 12:09:43.863442  Product Page     Mobile               19   \n",
              "49998 2024-01-20 19:12:33.862904  Product Page     Mobile               27   \n",
              "49999 2024-01-13 20:14:36.458532  Product Page     Mobile               29   \n",
              "\n",
              "        Location  Revenue  Conversion  \n",
              "0        Chennai        0           0  \n",
              "1      New Delhi        0           0  \n",
              "2         Mumbai        0           0  \n",
              "3         Mumbai     1000           1  \n",
              "4         Mumbai        0           0  \n",
              "...          ...      ...         ...  \n",
              "49995  Hyderabad        0           0  \n",
              "49996     Mumbai        0           0  \n",
              "49997  New Delhi        0           0  \n",
              "49998    Kolkata        0           0  \n",
              "49999    Kolkata        0           0  \n",
              "\n",
              "[50000 rows x 9 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_treatment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FfaGq-C3IPu9"
      },
      "outputs": [],
      "source": [
        "df_control.to_csv(\"df_control.csv\")\n",
        "df_treatment.to_csv(\"df_treatment.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FtMpGKBg9Swg"
      },
      "source": [
        "Now since, I have a dataset, Let's perform\n",
        "\n",
        "### Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r0P54JC99ngM"
      },
      "source": [
        "One of the most important createria for A/B Testing is that there should be no same person on both the variants of the same test.\n",
        "\n",
        "\n",
        "So, lets' check."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AfDUIdhl0hQJ"
      },
      "source": [
        "\n",
        "### Checking for overlapping user in more than one variant of the AB test?\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h_NAUANCzBfe",
        "outputId": "2e9f8777-04bd-49d4-a69e-3f8418aba88c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "No Overlapping Users Found\n"
          ]
        }
      ],
      "source": [
        "# Check for overlapping users\n",
        "overlapping_users = set(df_control['UserID']).intersection(set(df_treatment['UserID']))\n",
        "\n",
        "if len(overlapping_users) > 0:\n",
        "    print(f\"Overlapping Users Found: {len(overlapping_users)}\")\n",
        "else:\n",
        "    print(\"No Overlapping Users Found\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LE-OaM1-_xIb"
      },
      "source": [
        "Now, that it is proved that we are working with unique users per dataset.\n",
        "\n",
        "Let's understand our data more!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fx7v18sguh7j",
        "outputId": "f8716537-d21e-44ea-9016-b08168f24b04"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 50000 entries, 0 to 49999\n",
            "Data columns (total 9 columns):\n",
            " #   Column           Non-Null Count  Dtype         \n",
            "---  ------           --------------  -----         \n",
            " 0   UserID           50000 non-null  object        \n",
            " 1   Group            50000 non-null  object        \n",
            " 2   Timestamp        50000 non-null  datetime64[ns]\n",
            " 3   PageViewed       50000 non-null  object        \n",
            " 4   DeviceType       50000 non-null  object        \n",
            " 5   SessionDuration  50000 non-null  int64         \n",
            " 6   Location         50000 non-null  object        \n",
            " 7   Revenue          50000 non-null  int64         \n",
            " 8   Conversion       50000 non-null  int64         \n",
            "dtypes: datetime64[ns](1), int64(3), object(5)\n",
            "memory usage: 3.4+ MB\n"
          ]
        }
      ],
      "source": [
        "# Get information about the dataset\n",
        "df_control.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AW2Ba6k6AGCE"
      },
      "source": [
        "### Conclusions:\n",
        "\n",
        "1. We have three different dtypes: *datetime64[ns](1), int64(3), object(5)*. So, I don't need to perform any dtypes conversions as all the columns dtypes makes sense to me.\n",
        "\n",
        "\n",
        "2. No null columns, meaning no missing data. Hence, I don't need to  perform pre-proccessing \"missing_value\".\n",
        "\n",
        "\n",
        "Since, we have same columns in both the tables.\n",
        "\n",
        "I will skip the treatment group info(). You can check it out for your understanding.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "pDusO6kt-YBX",
        "outputId": "22d7e0dd-7b9e-461e-ec77-bbc5e2ec62b8"
      },
      "outputs": [
        {
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              "        document.querySelector('#df-079de6b2-be9d-49f7-8e29-f26a884716f0 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-079de6b2-be9d-49f7-8e29-f26a884716f0');\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-bbe0deed-969d-4c4b-a22a-82fc3ab9b6df\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bbe0deed-969d-4c4b-a22a-82fc3ab9b6df')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bbe0deed-969d-4c4b-a22a-82fc3ab9b6df button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                           Timestamp  SessionDuration       Revenue  \\\n",
              "count                          50000     50000.000000  50000.000000   \n",
              "mean   2024-01-15 22:46:45.156093440        15.069860    165.470000   \n",
              "min       2024-01-01 00:00:11.972070         1.000000      0.000000   \n",
              "25%    2024-01-08 09:27:57.733941248         8.000000      0.000000   \n",
              "50%    2024-01-15 21:39:54.386138368        15.000000      0.000000   \n",
              "75%    2024-01-23 11:22:57.847912960        22.000000      0.000000   \n",
              "max       2024-01-30 23:59:36.884095        29.000000   4000.000000   \n",
              "std                              NaN         8.380066    635.501972   \n",
              "\n",
              "         Conversion  \n",
              "count  50000.000000  \n",
              "mean       0.098460  \n",
              "min        0.000000  \n",
              "25%        0.000000  \n",
              "50%        0.000000  \n",
              "75%        0.000000  \n",
              "max        1.000000  \n",
              "std        0.297939  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get summary statistics of the dataset\n",
        "df_control.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ITBtTp3U-dJ-",
        "outputId": "4beba8c4-4cfc-460a-c8d4-d84ba1788c09"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df_treatment\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Timestamp\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000050\",\n        \"max\": \"2024-01-30 23:59:17.203705\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"50000\",\n          \"2024-01-15 23:59:20.808627712\",\n          \"2024-01-23 11:54:53.252828672\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SessionDuration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17672.704550834304,\n        \"min\": 1.0,\n        \"max\": 50000.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          14.96112,\n          22.0,\n          50000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Revenue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17472.58791899938,\n        \"min\": 0.0,\n        \"max\": 50000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          280.7,\n          836.5832340121027,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Conversion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17677.592198535363,\n        \"min\": 0.0,\n        \"max\": 50000.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.16236,\n          0.36878442271740386,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-6a5f4cb4-954f-46a2-84bb-38e3974ae29f\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Timestamp</th>\n",
              "      <th>SessionDuration</th>\n",
              "      <th>Revenue</th>\n",
              "      <th>Conversion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>50000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2024-01-15 23:59:20.808627712</td>\n",
              "      <td>14.961120</td>\n",
              "      <td>280.700000</td>\n",
              "      <td>0.162360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2024-01-01 00:01:56.687191</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2024-01-08 12:22:13.159630336</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2024-01-15 23:45:10.590503424</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2024-01-23 11:54:53.252828672</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2024-01-30 23:59:17.203705</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>4000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>8.382654</td>\n",
              "      <td>836.583234</td>\n",
              "      <td>0.368784</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6a5f4cb4-954f-46a2-84bb-38e3974ae29f')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-6a5f4cb4-954f-46a2-84bb-38e3974ae29f 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-6a5f4cb4-954f-46a2-84bb-38e3974ae29f');\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-de64c584-5d99-4a9f-995b-dd23195934e6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-de64c584-5d99-4a9f-995b-dd23195934e6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-de64c584-5d99-4a9f-995b-dd23195934e6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                           Timestamp  SessionDuration       Revenue  \\\n",
              "count                          50000     50000.000000  50000.000000   \n",
              "mean   2024-01-15 23:59:20.808627712        14.961120    280.700000   \n",
              "min       2024-01-01 00:01:56.687191         1.000000      0.000000   \n",
              "25%    2024-01-08 12:22:13.159630336         8.000000      0.000000   \n",
              "50%    2024-01-15 23:45:10.590503424        15.000000      0.000000   \n",
              "75%    2024-01-23 11:54:53.252828672        22.000000      0.000000   \n",
              "max       2024-01-30 23:59:17.203705        29.000000   4000.000000   \n",
              "std                              NaN         8.382654    836.583234   \n",
              "\n",
              "         Conversion  \n",
              "count  50000.000000  \n",
              "mean       0.162360  \n",
              "min        0.000000  \n",
              "25%        0.000000  \n",
              "50%        0.000000  \n",
              "75%        0.000000  \n",
              "max        1.000000  \n",
              "std        0.368784  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get summary statistics of the dataset\n",
        "df_treatment.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Aq6gqAD-pZW"
      },
      "source": [
        "### Conclusion:\n",
        "\n",
        "It's clear that, `df_treatment` has more variance for both \"Revenue\" and \"Conversion\", meaning there could be a significant difference in Conversion Rates between groups.\n",
        "\n",
        "This is a good sign but this is not enough evidence for us to confidently go with the new feature, as we have not measured how confident we are in this result.  \n",
        "\n",
        "So, let's do some EDA."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LOe1eaNCDAtb"
      },
      "source": [
        "### Calculate conversion rates\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "be3YDDP3ufFv",
        "outputId": "5808e6c9-6e2e-4334-c915-b29b6458c614"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Control Conversion Rate: 9.8460\n"
          ]
        }
      ],
      "source": [
        "# Calculate conversion rates\n",
        "control_conversions = sum(1 for c in control_data['Conversion'] if c == 1)\n",
        "\n",
        "# Total number of users in each group\n",
        "control_users = len(control_data['UserID']) # Access the 'UserID' list to get the count\n",
        "\n",
        "# Conversion rates\n",
        "control_conversion_rate = control_conversions / control_users * 100\n",
        "\n",
        "print(f\"Control Conversion Rate: {control_conversion_rate:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PnBFzEaYuh-c",
        "outputId": "501e7830-6eb8-475c-8d54-f69a36577b74"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Treatment Conversion Rate: 16.2360\n"
          ]
        }
      ],
      "source": [
        "# Calculate conversion rates\n",
        "treatment_conversions = sum(1 for c in treatment_data['Conversion'] if c == 1)\n",
        "\n",
        "# Total number of users in each group\n",
        "treatment_users = len(treatment_data['UserID']) # Access the 'UserID' list to get the count\n",
        "\n",
        "# Conversion rates\n",
        "treatment_conversion_rate = treatment_conversions / treatment_users * 100\n",
        "\n",
        "print(f\"Treatment Conversion Rate: {treatment_conversion_rate:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "EBjfNOfKChrb"
      },
      "outputs": [],
      "source": [
        "# Concatenate the control and treatment dataframes\n",
        "df = pd.concat([df_control, df_treatment])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "qSRJDw_pBo4g"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k4fpfgWEC-3X"
      },
      "source": [
        "### Plot Conversion Rates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "lh4KF7BJBrpm",
        "outputId": "9fee0724-ccfe-4cf6-ee77-52f5a952cc72"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-34-de6f3bb28778>:6: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x='Group', y='Conversion', data=conversion_rates, palette=['skyblue', 'orange'])\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Calculate conversion rates for each group\n",
        "conversion_rates = df.groupby('Group')['Conversion'].mean().reset_index()\n",
        "\n",
        "# Plot conversion rates\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='Group', y='Conversion', data=conversion_rates, palette=['skyblue', 'orange'])\n",
        "plt.title('Conversion Rates by Group')\n",
        "plt.ylabel('Conversion Rate')\n",
        "plt.xlabel('Group')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9QLhQ8mlxCge"
      },
      "source": [
        "### Average Revenue Per User\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LB7IdTEaLo35",
        "outputId": "60dfdba4-d1e1-4f6d-f26c-e325d026bb13"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Control ARPU: 1680.58\n",
            "Treatment ARPU: 1728.87\n"
          ]
        }
      ],
      "source": [
        "# Calculate ARPU for Control and Treatment groups\n",
        "control_revenue = sum(control_data['Revenue'])\n",
        "treatment_revenue = sum(treatment_data['Revenue'])\n",
        "\n",
        "control_arpu = control_revenue / control_conversions if control_conversions > 0 else 0\n",
        "treatment_arpu = treatment_revenue / treatment_conversions if treatment_conversions > 0 else 0\n",
        "\n",
        "print(f\"Control ARPU: {control_arpu:.2f}\")\n",
        "print(f\"Treatment ARPU: {treatment_arpu:.2f}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mu19hC0sJq0l"
      },
      "source": [
        "### User Engagement Percentage\n",
        "User engagement can be interpreted as the improvement in the conversion rate of the treatment group compared to the control group.\n",
        "\n",
        "Similarily,\n",
        "\n",
        "### Revenue Percentage\n",
        "Revenue percentage improvement can be interpreted as the improvement in ARPU of the treatment group compared to the control group."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KLk5XJ3PIXlU",
        "outputId": "ac03b48b-bd89-4f13-8506-6d1aca9c38fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "User Engagement Percentage: 64.77%\n",
            "Revenue Percentage: 2.87%\n"
          ]
        }
      ],
      "source": [
        "# Given data\n",
        "control_conversion_rate = 0.0985\n",
        "treatment_conversion_rate = 0.1623\n",
        "control_arpu = 1680.58\n",
        "treatment_arpu = 1728.87\n",
        "\n",
        "# Calculate User Engagement Percentage\n",
        "user_engagement_percentage = ((treatment_conversion_rate - control_conversion_rate) / control_conversion_rate) * 100\n",
        "\n",
        "# Calculate Revenue Percentage\n",
        "revenue_percentage = ((treatment_arpu - control_arpu) / control_arpu) * 100\n",
        "\n",
        "print(f\"User Engagement Percentage: {user_engagement_percentage:.2f}%\")\n",
        "print(f\"Revenue Percentage: {revenue_percentage:.2f}%\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qxuk0bAkBnck"
      },
      "source": [
        "### Time Series Analysis\n",
        "\n",
        "Let’s look at `Timestep` metric, to estimate how long it will need to achieve the the target results depending upon duration of the experiment.\n",
        "\n",
        "It is necessary because the timestamp is really crucial for A/B testing as it helps us understand that whether or not we need to increase the Campaign time (which is a costly thing).\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "id": "Ffc2M9iWERqd",
        "outputId": "b509a4b4-3200-4188-fd54-7875aa7415fa"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1400x700 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Extract date from timestamp\n",
        "df['Date'] = df['Timestamp'].dt.date\n",
        "\n",
        "# Calculate daily conversion rates for each group\n",
        "daily_conversion_rates = df.groupby(['Date', 'Group'])['Conversion'].mean().reset_index()\n",
        "\n",
        "# Plot daily conversion rates\n",
        "plt.figure(figsize=(14, 7))\n",
        "sns.lineplot(x='Date', y='Conversion', hue='Group', data=daily_conversion_rates)\n",
        "plt.title('Daily Conversion Rates by Group')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Conversion Rate')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VDckzv4xFeJ_"
      },
      "source": [
        "### Conclusion\n",
        "\n",
        "Clearly, treatment group has better engagement.\n",
        "\n",
        "Let's perform\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qq_ynXwwWTUs"
      },
      "source": [
        "### Statistical Test\n",
        "\n",
        "Using **Chi-square test  χ² of Independences**\n",
        "\n",
        " This test is a nonparametric hypothesis test, used to determine if there is a significant association between two categorical variables.\n",
        "\n",
        " Here, in this case, the two variables are:\n",
        "\n",
        "- Group (Control vs. Treatment)\n",
        "- Conversion (0 for no purchase, 1 for purchase)\n",
        "\n",
        "By constructing a contingency table that cross-tabulates the counts of conversions and non-conversions for both control and treatment groups, the chi-square test assesses whether the observed differences in conversion rates between the groups are statistically significant or if they could have occurred by chance.\n",
        "\n",
        "\n",
        "\n",
        "**Hypothesis Formulation:**\n",
        "\n",
        "- **Null Hypothesis (𝐻0)**: There is no difference in conversion rates or revenue between the control and treatment groups.\n",
        "- **Alternative Hypothesis (𝐻1)**: The treatment group (with 360-degree videos) has a higher conversion rate or revenue than the control group.\n",
        "\n",
        "\n",
        "**P-Value:**\n",
        "To assess the significance of the result, I'll consider the typical p-value (< 0.05) indicates that the differences are unlikely to have occurred by chance, suggesting a significant association between the group and conversion rate.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "3I2dLFdHLDvP"
      },
      "outputs": [],
      "source": [
        "import scipy.stats as stats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qlmartYMLowb",
        "outputId": "243616f3-6f02-439b-a115-0e55e04b665e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Chi-square statistic: 899.59\n",
            "P-value: 1.2051806921819425e-197\n",
            "There is a statistically significant difference in Conversion Rates between groups.\n"
          ]
        }
      ],
      "source": [
        "# Create contingency table\n",
        "contingency_table = pd.crosstab(df['Group'], df['Conversion'])\n",
        "\n",
        "# Perform chi-square test\n",
        "chi2, p, _, _ = stats.chi2_contingency(contingency_table)\n",
        "\n",
        "print(f\"Chi-square statistic: {chi2:.2f}\")\n",
        "print(f\"P-value: {p}\")\n",
        "\n",
        "# Determine statistical significance\n",
        "alpha = 0.05\n",
        "if p < alpha:\n",
        "    print(\"There is a statistically significant difference in Conversion Rates between groups.\")\n",
        "else:\n",
        "    print(\"There is no statistically significant difference in Conversion Rates between groups.\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XGfunSfnRWLJ"
      },
      "source": [
        "For the chi-square test, the concept of the **Minimal Detectable Effect (MDE)** can be translated into terms of expected frequencies or proportions in contingency tables.\n",
        "\n",
        " While the exact computation of MDE for chi-square tests is more complex, I am approaching it by considering the expected changes in the proportions of the contingency table.\n",
        "\n",
        "### Visualizing Chi-Square Test Results for Practical Significance\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "pT_3WmyULot7",
        "outputId": "39d73d17-c252-4008-a179-02e01a0ada93"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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++2+7b/wkqXfv3qpQoYJq1qypggULasOGDZmup1evXvr00081e/ZsValSRQ0bNlRERIRdA3cnTzzxhF5++WV17NhRBQsW1IQJEzJdDwDcDv0T8PCih4KVXExGZjIEAAAAAAAAshBXSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMsRSgEAAAAAAMByhFIAAAAAAACwHKEUAAAAAAAALEcoBQAAAAAAAMv9PzAPyeidE6DeAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1200x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualize the observed and expected frequencies\n",
        "observed = contingency_table.values\n",
        "expected = expected\n",
        "\n",
        "fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n",
        "\n",
        "# Bar plot of expected frequencies\n",
        "ax[0].bar(['Control', 'Treatment'], expected[:, 0], label='No Conversion', color='skyblue')\n",
        "ax[0].bar(['Control', 'Treatment'], expected[:, 1], bottom=expected[:, 0], label='Conversion', color='orange')\n",
        "ax[0].set_title('Expected Frequencies')\n",
        "ax[0].set_ylabel('Frequency')\n",
        "ax[0].legend()\n",
        "\n",
        "# Bar plot of observed frequencies\n",
        "ax[1].bar(['Control', 'Treatment'], observed[:, 0], label='No Conversion', color='skyblue')\n",
        "ax[1].bar(['Control', 'Treatment'], observed[:, 1], bottom=observed[:, 0], label='Conversion', color='orange')\n",
        "ax[1].set_title('Observed Frequencies')\n",
        "ax[1].set_ylabel('Frequency')\n",
        "ax[1].legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z15cI6OuSeZL"
      },
      "source": [
        "#### Mosaic Plot\n",
        "\n",
        "Another effective way to visualize the results of a chi-square test is a mosaic plot. As it provides a graphical representation of the association between categorical variables.\n",
        "\n",
        "Let's plot!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tIbuZs8yQnkp"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "from statsmodels.graphics.mosaicplot import mosaic"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "O9_eQc5CP6Xg",
        "outputId": "abb07f8d-469a-4cb1-f6db-d3f951280995"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x600 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Prepare data for mosaic plot\n",
        "data = {\n",
        "    ('Control', 'No Conversion'): observed[0, 0],\n",
        "    ('Control', 'Conversion'): observed[0, 1],\n",
        "    ('Treatment', 'No Conversion'): observed[1, 0],\n",
        "    ('Treatment', 'Conversion'): observed[1, 1]\n",
        "}\n",
        "\n",
        "# Plot mosaic plot\n",
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "mosaic(data, title='Mosaic Plot of Conversion Rates by Group', ax=ax)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0a9dxPA1Fq8j"
      },
      "source": [
        "# Results and Final Conculsion\n",
        "\n",
        "So, a low p-value indicates that there is a statistically significant difference in Conversion Rates between groups, as the observed differences between the two versions are unlikely to be due to chance.\n",
        "\n",
        "\n",
        "The conducted A/B test demonstrated a significant positive effect on user engagement and revenue due to 360-degree product videos on the product page.\n",
        "\n",
        "With 95% confidence level, the treatment group (which included the 360-degree product videos), exhibited a substantial increase of **64.77% in user interaction** and **2.87% rise in overall revenue** compared to the control group.\n",
        "\n",
        "I believe, this feature not only improve the user interaction, after fully launching but also contributed to a notable increase in overall sales performance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uWshpmufQfcl"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}