{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "2021-06-27-ad-conversion-simple-ab-testing.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNxngMnYxHhMWMH/afqaSew"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jzX21dhMG60W"
      },
      "source": [
        "# Ad-conversion A-B Testing\n",
        "> Simple T-test to compare 2 samples of ad-conversions\n",
        "\n",
        "- toc: true\n",
        "- badges: true\n",
        "- comments: true\n",
        "- categories: [ABTest]\n",
        "- image:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vQ13VFgf49GE",
        "outputId": "31f18908-5d9a-4b87-d4f2-1bb3dd397b33"
      },
      "source": [
        "!wget https://github.com/sparsh-ai/reco-data/raw/master/abtest-sample-dayC1C2.csv"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-06-27 14:34:04--  https://github.com/sparsh-ai/reco-data/raw/master/abtest-sample-dayC1C2.csv\n",
            "Resolving github.com (github.com)... 140.82.112.3\n",
            "Connecting to github.com (github.com)|140.82.112.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/sparsh-ai/reco-data/master/abtest-sample-dayC1C2.csv [following]\n",
            "--2021-06-27 14:34:04--  https://raw.githubusercontent.com/sparsh-ai/reco-data/master/abtest-sample-dayC1C2.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.111.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 408 [text/plain]\n",
            "Saving to: ‘abtest-sample-dayC1C2.csv’\n",
            "\n",
            "abtest-sample-dayC1 100%[===================>]     408  --.-KB/s    in 0s      \n",
            "\n",
            "2021-06-27 14:34:04 (13.0 MB/s) - ‘abtest-sample-dayC1C2.csv’ saved [408/408]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6j04Iy0N5C7h"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import scipy.stats as ss "
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "4ytG1m5A5Dy3",
        "outputId": "d5152c41-62ec-419b-9967-eae6a685b8b1"
      },
      "source": [
        "data= pd.read_csv(\"abtest-sample-dayC1C2.csv\")\n",
        "data.head(10)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Day</th>\n",
              "      <th>Conversion_A</th>\n",
              "      <th>Conversion_B</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Day   Conversion_A  Conversion_B\n",
              "0     1          0.15          0.19\n",
              "1     2          0.12          0.20\n",
              "2     3          0.18          0.18\n",
              "3     4          0.19          0.22\n",
              "4     5          0.17          0.19\n",
              "5     6          0.13          0.17\n",
              "6     7          0.14          0.18\n",
              "7     8          0.23          0.20\n",
              "8     9          0.11          0.17\n",
              "9    10          0.15          0.22"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OO0ehoqj5L0j"
      },
      "source": [
        "Let’s plot the distribution of target and control group:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "Jok5xwJD5JAX",
        "outputId": "c6bba329-983e-4264-d8b0-c8ed7c93bdc6"
      },
      "source": [
        "sns.distplot(data.Conversion_A)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8a7d87c1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "JVZ7sxHM5Nbv",
        "outputId": "63f18dc1-c6dd-411e-8207-c139125dee6d"
      },
      "source": [
        "sns.distplot(data.Conversion_B)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8a7ac61150>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i00JbDz15QsV"
      },
      "source": [
        "Now, we will perform the t-test:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QOuEKhy05PGB",
        "outputId": "c1c4fb0d-8ac9-4e19-c17e-45b4d4f19878"
      },
      "source": [
        "t_stat, p_val= ss.ttest_ind(data.Conversion_B,data.Conversion_A)\n",
        "t_stat , p_val"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3.78736793091929, 0.000363796012828762)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wEZk66_4GKyQ"
      },
      "source": [
        "For our example, the observed value i.e the mean of the test group is 0.19. The hypothesized value (Mean of the control group) is 0.16. On the calculation of the t-score, we get the t-score as .3787. and the p-value is 0.00036.\n",
        "\n",
        "SO what does all this mean for our A/B Testing?\n",
        "\n",
        "Here, our p-value is less than the significance level i.e 0.05. Hence, we can reject the null hypothesis. This means that in our A/B testing, newsletter B is performing better than newsletter A. So our recommendation would be to replace our current newsletter with B to bring more traffic on our website."
      ]
    }
  ]
}