Copia_de_ENews_Express_Learner_Notebook_5BFull_Code_Version_5D.ipynb

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      "source": [
        "# Project Business Statistics: E-news Express\n",
        "\n",
        "**Marks: 60**"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "SrLd-ICEmPSD",
      "metadata": {
        "id": "SrLd-ICEmPSD"
      },
      "source": [
        "## Define Problem Statement and Objectives"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "9Vt1PLLqmXNn",
      "metadata": {
        "id": "9Vt1PLLqmXNn"
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      "source": [
        "##Business Context\n",
        "The advent of e-news, or electronic news, portals has offered us a great opportunity to quickly get updates on the day-to-day events occurring globally. The information on these portals is retrieved electronically from online databases, processed using a variety of software, and then transmitted to the users. There are multiple advantages of transmitting new electronically, like faster access to the content and the ability to utilize different technologies such as audio, graphics, video, and other interactive elements that are either not being used or aren’t common yet in traditional newspapers.\n",
        "\n",
        "E-news Express, an online news portal, aims to expand its business by acquiring new subscribers. With every visitor to the website taking certain actions based on their interest, the company plans to analyze these actions to understand user interests and determine how to drive better engagement. The executives at E-news Express are of the opinion that there has been a decline in new monthly subscribers compared to the past year because the current webpage is not designed well enough in terms of the outline & recommended content to keep customers engaged long enough to make a decision to subscribe.\n",
        "\n",
        "[Companies often analyze user responses to two variants of a product to decide which of the two variants is more effective. This experimental technique, known as A/B testing, is used to determine whether a new feature attracts users based on a chosen metric.]\n",
        "\n",
        "##Objective\n",
        "The design team of the company has researched and created a new landing page that has a new outline & more relevant content shown compared to the old page. In order to test the effectiveness of the new landing page in gathering new subscribers, the Data Science team conducted an experiment by randomly selecting 100 users and dividing them equally into two groups. The existing landing page was served to the first group (control group) and the new landing page to the second group (treatment group). Data regarding the interaction of users in both groups with the two versions of the landing page was collected. Being a data scientist in E-news Express, you have been asked to explore the data and perform a statistical analysis (at a significance level of 5%) to determine the effectiveness of the new landing page in gathering new subscribers for the news portal by answering the following questions:\n",
        "\n",
        " \n",
        "\n",
        "1.   Do the users spend more time on the new landing page than on the existing landing page?\n",
        "2.   Is the conversion rate (the proportion of users who visit the landing page and get converted) for the new page greater than the conversion rate for the old page?\n",
        "3.   Does the converted status depend on the preferred language?\n",
        "4.   Is the time spent on the new page the same for the different language users?\n",
        " \n",
        "\n",
        "##Data Dictionary\n",
        "The data contains information regarding the interaction of users in both groups with the two versions of the landing page.\n",
        "\n",
        "1.  user_id - Unique user ID of the person visiting the website\n",
        "2.  group - Whether the user belongs to the first group (control) or the second  group (treatment)\n",
        "3.  landing_page - Whether the landing page is new or old\n",
        "4.  time_spent_on_the_page - Time (in minutes) spent by the user on the landing page\n",
        "5.  converted - Whether the user gets converted to a subscriber of the news portal or not\n",
        "6.  language_preferred - Language chosen by the user to view the landing page"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "a6a3d218",
      "metadata": {
        "id": "a6a3d218"
      },
      "source": [
        "## Import all the necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "658c5dec",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "658c5dec",
        "outputId": "d995d08b-a952-4522-b2e0-73acf1e4029a"
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      "outputs": [],
      "source": [
        "# Libraries to help with reading and manipulating data\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Libraries to help with data visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline \n",
        "\n",
        "# Library to help with statistical analysis\n",
        "import scipy.stats as stats "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "6970dd99",
      "metadata": {
        "id": "6970dd99"
      },
      "source": [
        "## Reading the Data into a DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "d3b95640",
      "metadata": {
        "id": "d3b95640"
      },
      "outputs": [],
      "source": [
        "# read the data\n",
        "data = pd.read_csv('abtest.csv')\n",
        "df=data.copy()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "9d75cfa2",
      "metadata": {
        "id": "9d75cfa2"
      },
      "source": [
        "## Explore the dataset and extract insights using Exploratory Data Analysis"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "27201534",
      "metadata": {
        "id": "27201534"
      },
      "source": [
        "- Data Overview\n",
        "  - Viewing the first and last few rows of the dataset\n",
        "  - Checking the shape of the dataset\n",
        "  - Getting the statistical summary for the variables\n",
        "- Check for missing values\n",
        "- Check for duplicates"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "e1ad11d4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "e1ad11d4",
        "outputId": "47468b48-33d2-4d95-fd48-60fed1e5740d"
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>group</th>\n",
              "      <th>landing_page</th>\n",
              "      <th>time_spent_on_the_page</th>\n",
              "      <th>converted</th>\n",
              "      <th>language_preferred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>546592</td>\n",
              "      <td>control</td>\n",
              "      <td>old</td>\n",
              "      <td>3.48</td>\n",
              "      <td>no</td>\n",
              "      <td>Spanish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>546468</td>\n",
              "      <td>treatment</td>\n",
              "      <td>new</td>\n",
              "      <td>7.13</td>\n",
              "      <td>yes</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>546462</td>\n",
              "      <td>treatment</td>\n",
              "      <td>new</td>\n",
              "      <td>4.40</td>\n",
              "      <td>no</td>\n",
              "      <td>Spanish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>546567</td>\n",
              "      <td>control</td>\n",
              "      <td>old</td>\n",
              "      <td>3.02</td>\n",
              "      <td>no</td>\n",
              "      <td>French</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>546459</td>\n",
              "      <td>treatment</td>\n",
              "      <td>new</td>\n",
              "      <td>4.75</td>\n",
              "      <td>yes</td>\n",
              "      <td>Spanish</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   user_id      group landing_page  time_spent_on_the_page converted  \\\n",
              "0   546592    control          old                    3.48        no   \n",
              "1   546468  treatment          new                    7.13       yes   \n",
              "2   546462  treatment          new                    4.40        no   \n",
              "3   546567    control          old                    3.02        no   \n",
              "4   546459  treatment          new                    4.75       yes   \n",
              "\n",
              "  language_preferred  \n",
              "0            Spanish  \n",
              "1            English  \n",
              "2            Spanish  \n",
              "3             French  \n",
              "4            Spanish  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "Bi8B0svIkC_r",
      "metadata": {
        "colab": {
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        "id": "Bi8B0svIkC_r",
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              "    }\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>user_id</th>\n",
              "      <th>group</th>\n",
              "      <th>landing_page</th>\n",
              "      <th>time_spent_on_the_page</th>\n",
              "      <th>converted</th>\n",
              "      <th>language_preferred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>546446</td>\n",
              "      <td>treatment</td>\n",
              "      <td>new</td>\n",
              "      <td>5.15</td>\n",
              "      <td>no</td>\n",
              "      <td>Spanish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>546544</td>\n",
              "      <td>control</td>\n",
              "      <td>old</td>\n",
              "      <td>6.52</td>\n",
              "      <td>yes</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>546472</td>\n",
              "      <td>treatment</td>\n",
              "      <td>new</td>\n",
              "      <td>7.07</td>\n",
              "      <td>yes</td>\n",
              "      <td>Spanish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>546481</td>\n",
              "      <td>treatment</td>\n",
              "      <td>new</td>\n",
              "      <td>6.20</td>\n",
              "      <td>yes</td>\n",
              "      <td>Spanish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>546483</td>\n",
              "      <td>treatment</td>\n",
              "      <td>new</td>\n",
              "      <td>5.86</td>\n",
              "      <td>yes</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    user_id      group landing_page  time_spent_on_the_page converted  \\\n",
              "95   546446  treatment          new                    5.15        no   \n",
              "96   546544    control          old                    6.52       yes   \n",
              "97   546472  treatment          new                    7.07       yes   \n",
              "98   546481  treatment          new                    6.20       yes   \n",
              "99   546483  treatment          new                    5.86       yes   \n",
              "\n",
              "   language_preferred  \n",
              "95            Spanish  \n",
              "96            English  \n",
              "97            Spanish  \n",
              "98            Spanish  \n",
              "99            English  "
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "2D0EeagAlX52",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2D0EeagAlX52",
        "outputId": "16fac2f5-8555-42e6-8b3a-0b157ef8f33b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100 entries, 0 to 99\n",
            "Data columns (total 6 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   user_id                 100 non-null    int64  \n",
            " 1   group                   100 non-null    object \n",
            " 2   landing_page            100 non-null    object \n",
            " 3   time_spent_on_the_page  100 non-null    float64\n",
            " 4   converted               100 non-null    object \n",
            " 5   language_preferred      100 non-null    object \n",
            "dtypes: float64(1), int64(1), object(4)\n",
            "memory usage: 4.8+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "zMVKEwChjxrg",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zMVKEwChjxrg",
        "outputId": "a9b71d65-a711-47aa-808b-fa6230b59191"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "There are 100 Rows, and 6 columns\n"
          ]
        }
      ],
      "source": [
        "print('There are',df.shape[0],'Rows, and', df.shape[1], 'columns') #.Shape[0] give us the rows and shape[1] the columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "vUZkJOeRkInT",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "vUZkJOeRkInT",
        "outputId": "64e7833e-30fc-4460-a582-9cc21798c83a"
      },
      "outputs": [
        {
          "data": {
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              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <td>100.0</td>\n",
              "      <td>546517.0000</td>\n",
              "      <td>52.295779</td>\n",
              "      <td>546443.00</td>\n",
              "      <td>546467.75</td>\n",
              "      <td>546492.500</td>\n",
              "      <td>546567.2500</td>\n",
              "      <td>546592.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time_spent_on_the_page</th>\n",
              "      <td>100.0</td>\n",
              "      <td>5.3778</td>\n",
              "      <td>2.378166</td>\n",
              "      <td>0.19</td>\n",
              "      <td>3.88</td>\n",
              "      <td>5.415</td>\n",
              "      <td>7.0225</td>\n",
              "      <td>10.71</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        count         mean        std        min        25%  \\\n",
              "user_id                 100.0  546517.0000  52.295779  546443.00  546467.75   \n",
              "time_spent_on_the_page  100.0       5.3778   2.378166       0.19       3.88   \n",
              "\n",
              "                               50%          75%        max  \n",
              "user_id                 546492.500  546567.2500  546592.00  \n",
              "time_spent_on_the_page       5.415       7.0225      10.71  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "3GWB4eu6kYpB",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "3GWB4eu6kYpB",
        "outputId": "b0667b31-0de5-4378-c531-2c26047bffbd"
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      "outputs": [
        {
          "data": {
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              "<div>\n",
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              "    .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>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>user_id</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>group</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>landing_page</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>time_spent_on_the_page</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>converted</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>language_preferred</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Count\n",
              "user_id                     0\n",
              "group                       0\n",
              "landing_page                0\n",
              "time_spent_on_the_page      0\n",
              "converted                   0\n",
              "language_preferred          0"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.DataFrame({'Count': df.isnull().sum()})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "qZ5ageGVkeRn",
      "metadata": {
        "colab": {
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        },
        "id": "qZ5ageGVkeRn",
        "outputId": "ef542573-40a1-481e-93e3-0d35963e2769"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "duplicates=df.duplicated().sum()\n",
        "duplicates"
      ]
    },
    {
      "attachments": {},
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      "id": "5VSGT96-jgJz",
      "metadata": {
        "id": "5VSGT96-jgJz"
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      "source": [
        "#### Observations:\n",
        "\n",
        "- There are 6 columns with 100 rows and no missing values.\n",
        "- There are no duplicate values.\n",
        "- The column user_id is interpreted as a numerical column but is actually categorical.\n",
        "- The only numerical data type is time_spent_on_the_page.\n",
        "- Average time spent on the page is around 5 min 38 sec.\n",
        "- Min time spent on the page is 19 sec\n",
        "- Max time spent on the page is 10 min 71 sec\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "68f3b2c9",
      "metadata": {
        "id": "68f3b2c9"
      },
      "source": [
        "### Univariate Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "1932d0f3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1932d0f3",
        "outputId": "b9084c76-bc10-4876-947c-c68af13e01c3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['user_id'].nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "EBxiD-JyB9ID",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 913
        },
        "id": "EBxiD-JyB9ID",
        "outputId": "4f33c98f-ef58-42bd-fd5a-ea07c1054b8b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set_style(\"whitegrid\")\n",
        "plt.figure(figsize = (7,5))          \n",
        "sns.set_palette(\"icefire_r\")\n",
        "sns.histplot(data=df,x='time_spent_on_the_page',kde=True)\n",
        "plt.show()\n",
        "plt.figure(figsize = (7,5)) \n",
        "sns.boxplot(data=df,x='time_spent_on_the_page')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "wOU78b1jyZNY",
      "metadata": {
        "id": "wOU78b1jyZNY"
      },
      "source": [
        "### Observations\n",
        "\n",
        "- According to the graphs, time spent on the page apparently follows a normal distribution.\n",
        "- 75% of the users spent between 2 and 8 min on the page.\n",
        "- Maximum time spent on the page is a little bit more than 10 min."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "pOSGlfPg6Zvj",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pOSGlfPg6Zvj",
        "outputId": "9921f1fb-d4b7-4b04-ee91-17f19cafa0b2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Spanish    34\n",
              "French     34\n",
              "English    32\n",
              "Name: language_preferred, dtype: int64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['language_preferred'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "Q8wgNsqOl4W2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Q8wgNsqOl4W2",
        "outputId": "6a48ca37-c4b9-405f-9f37-928a969058a3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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Aluaxq3Ty8/O1cOFCSdLRo0dVXV2tTp06eao8AFiex87wx44dq1mzZmn8+PGy2WyaP39+o8s5AAD381ji+vn5admyZZ4qBwD4AT54BQAWQeADgEUQ+ABgEQQ+AFgEgQ8AFkHgA4BFEPgAYBEEPgBYBIEPABZB4AOARRD4AGARBD4AWASBDwAWQeADgEUQ+ABgEQQ+AFgEgQ8AFkHgA4BFEPgAYBEEPgBYBIEPABZB4AOARfh4qlB9fb3S0tL0xRdfyM/PT0899ZTCw8M9VR4ALM9jZ/jvvPOO6urqlJeXp0cffVQLFy70VGkAgDwY+Dt27NCtt94qSerXr59KSko8VRoAIA8u6VRXVysgIKDhtre3t5xOp3x8LjwFh8Oh0tJSl8f3/n+LLnmOaFpz+tFcsWvon5nM7J0k/fn/bzZ1fKtrTv8cDkejj3ks8AMCAlRTU9Nwu76+vtGwl77/VwAAwH08tqRz4403qrCwUJL06aef6uc//7mnSgMAJNkMwzA8UejsVTplZWUyDEPz58/Xtdde64nSAAB5MPABAC2LD14BgEUQ+ABgEQQ+AFiExy7LvJKsWbNG27Ztk9PplM1mU3Jysvr06XPJ406dOlVPP/30BR+bOXOmRo0apZiYmEuuA6m4uFhJSUnq0aNHw30dOnTQypUrXR5j1apVCgkJUb9+/VRQUKCpU6de8PuGDBmiv/3tb7Lb7Zc8b/zXwYMHNXr0aEVHRzfcN2jQoEb78FMkJCQoLS3tirnAhMBvpr179+rdd9/Vyy+/LJvNptLSUiUnJ2vLli2XPHZjYQ9z3HTTTcrIyLjkcaKiohQVFeWGGaG5evTooZycnJaeRqtB4DdTu3btdPjwYeXn5ysmJkZRUVHKz89XQkKCIiIiVF5eLsMwlJGRoeDgYM2ZM0f/+c9/VFFRoSFDhuhPf/qTZs6cKT8/Px06dEgVFRVauHChoqOjdcstt6ioqEgbNmzQa6+9Ji8vL11//fVKTU2VJOXl5Wnt2rWqrq5WWlqa+vbt28I/jStPQkKCrrvuOv373/9WdXW1VqxYoa5duyozM1PvvPOOgoODderUKT3yyCMNzykuLlZubq4yMjI0a9Ys7d+/X7W1tfrd736nMWPGSJLS0tJ08OBBSd8f2Nu3b98SL++KV1xcrKVLl8rX11fjxo3T1VdfrYyMDHl7e6tbt2568skn9Ze//EXvv/++amtr9dVXX2nKlCm66667tHPnTs2fP1/19fXq3Lmzli5dKknKzMxUVVWVTp06peXLl6tbt24t/Cp/Otbwm6lz587KysrSJ598onvuuUcjRozQ1q1bJX3/4bKcnByNHDlS2dnZOnLkiPr166fnnntO+fn5ys3NbRjn6quv1nPPPaeEhATl5eWdV2Pz5s2aPXu28vLyFBkZKafTKUmKjo7Wiy++qAkTJmjzZj7Kfqm2b9+uhISEhv/Wrl0rSerbt6+ef/553XLLLXrjjTf0+eef6x//+Ify8/OVmZmpysrKC45XXV2tjz/+WE8//bTWrl0rb2/vhsfuvvtu5eTkqGvXrioqKvLI67OCvXv3ntfDo0ePyuFw6KWXXtKdd96p2bNn6+mnn9b69evVuXNnvfrqq5K+71V2draysrK0Zs0aSdKcOXM0f/58vfLKKxo8eLD27dsnSRo8eLBefPFFxcTE6M0332yx1+oOnOE30/79+xUQEKAFCxZIkj777DNNmTJFnTp10k033STp++B/9913FRQUpM8++0zbt29XQECA6urqGsY5uwTQpUsXffLJJ+fVWLBggdatW6fFixerX79+OvtRibNrlSEhIaqtrTX9tV7pLrSk8/7776t3796Svu9NVVWV9u3bp+uvv17e3t7y9vZu9P2agIAApaSkaPbs2aqurtbo0aMbHjv7HHrnXj9c0ikuLlZERIQk6fjx46qoqFBSUpIkqba2Vr/61a8UHh6u6667TpIUGhra8P9lVVVVw1p9XFxcw5jn9q6qqsr012QmAr+ZvvjiC+Xl5SkrK0t+fn6KiIhQYGCgvL29VVJS0hDgPXr00ObNm9WuXTs9+eST2r9/vzZu3NgQ3jabrdEaGzdu1Lx582S32zV58mT961//uuhzYJ6zoVJfXy+n06k9e/Zc8PsqKiq0e/duZWZmyuFwaPDgwbrzzjsl0TtP8vL6fuGiQ4cO6tKli1avXq127dqpoKBAbdu21ZEjRy7Yj5/97Gf68ssv1b17d61Zs6bhwHElIfCbafjw4dq3b5/Gjh2rtm3byjAMPf7443rhhRf06quv6vnnn1ebNm20ePFiVVVV6dFHH9Wnn34qPz8/hYeHq6Ki4qI1evXqpfj4ePn7+6tz5876xS9+wRKOCc4u6ZzrQmffvXr10uDBgzVu3Dh16NBBvr6+F9z4r1OnTqqsrNS9994rLy8vJSYmNrlBIMzl5eWlJ554Qvfff78Mw5C/v78WL16sI0eOXPD7582bp5SUFHl5ealTp06aOHGiXnzxRQ/P2lxsreAmV9rlW/ivY8eO6c0339R9992nuro6/eY3v9ELL7ygq6++uqWnBjQLpx/ARXTo0EElJSW6++67ZbPZFBcXR9ijVeIMHwAsgssyAcAiCHwAsAgCHwAsgjdt0eLO3ZqgNRoyZIhCQ0Pl5eUlwzAUFBSkhQsXKiAg4KLP/fbbbzVx4kQFBQXpz3/+s9vntnTpUkVGRuquu+5y+9hofTjDB9xg3bp1ysnJ0fr16xUeHu7y5ybKysoUFhZmStgDP8QZPi5bH330kcsbX+3atUvz5s2Tv7+/OnbsKLvdrqlTp2r69OnauHGjJGncuHFavny5fHx8lJaWJofDocrKSiUlJWno0KHaunWrVq5cqYCAALVv3169evXSH//4Ry1btkz//Oc/VV9fr4kTJ2rkyJGNztkwDJ08eVIRERHavHmzNm3apPr6ek2bNk3ffPONnn/+eXl5eal///6aNm2annrqKVVUVGjlypWKi4vT7Nmz5XA4ZLfblZ6erjNnzuihhx5SUFCQYmJiVFhYqODgYH377bdas2aN0tLStH//ftXX1yspKUmDBg3S3//+d2VlZSk4OFinT59WZGSkp1qGy50BtLDt27cbSUlJ591XX19vDB8+3KiqqjIMwzAyMjKMvLw8Y9OmTUZiYqJhGIZRXl5uxMbGGoZhGGPGjDHKysoMwzCM5cuXG8nJycaBAweMuLi4hjHj4uKMAwcOGEVFRcb27dsNwzCMHTt2GBMnTjScTqcxZMgQo7Ky0jAMw5g+fbqxcuVK47333muYW21trTF69Gjj22+/PW+ut99+uxEfH29MmDDBSEhIMJYsWWKcPn3a2LRpk/Hggw8ahmEYX3/9tTFy5Ejju+++MwzDMGbMmGF88MEH5732Rx55xHjvvfcMwzCMbdu2GdOnTzcOHDhgDBo0yHA4HIZhGMaECROMt956yzAMw9iwYYOxePFiwzAM4/jx48aoUaOMuro647bbbjOOHz9u1NfXG7///e+NTZs2XUJ3cCXhDB+XpeZufFVRUaGePXtKkvr376+//vWvPxrT+L+PnHTq1ElZWVnKz8+XzWaT0+nU8ePHFRAQoJCQEEnSgAEDVFVVpbKyMu3evbthCwan06lDhw4pMDDwvLHXrVt3wT9wcnY/lq+++krHjx/X/fffL0mqqanRV199dd7Zd1lZmbKzs7V27VoZhtGwLUNYWJj8/Px+NGZZWZl27NihXbt2NcytsrJS7du3V4cOHSRJN9xwgws/bVgFgY/LUnM3vurSpYv27t2rHj16aOfOnZIku92uY8eO6cyZM6qpqWnYj37FihWKi4vT4MGDtWnTJr366qvq2LGjampqdPz4cQUHB2vnzp3q2rWrIiMjNWjQIKWnp6u+vl6rV69u1n7oZzfyCgsLU2hoqNatWydfX19t3rxZUVFROnHiRMP3RkZGKjExUTfeeKP27dunjz/++Lwxzjr7+iMjI9WlSxc9+OCDqq2tVVZWlkJCQnTixImG1/HZZ5+pS5cuzfjJ40pG4OOyUFRUdN6VJMuWLWvWxldz585VSkqK2rZtK19fX3Xu3FmdOnXSLbfcorFjx6pbt24KDw+XJI0YMUKLFy/WmjVr1KVLF3399dfy8vLS7NmzNWXKFLVr10719fUKDw/XkCFD9NFHHyk+Pl7fffedhg4d6tLVNz8UHBysiRMnKiEhQWfOnFHXrl01cuTIhrNzSUpOTm54b6G2tlZPPPFEk2Pee++9Sk1N1YQJE1RdXa34+Hj5+flpzpw5mjx5stq3b8/mbTgPWyvgirBhwwaNHDlSwcHBysjIkK+vb7P/tml2drYmTZokPz8/zZgxQ7/+9a8b/mIVcCXg8I8rQseOHZWYmKi2bduqXbt2WrhwYbPH8Pf317hx43TVVVepa9euGjVqlAkzBVoOZ/gAYBF88AoALILABwCLIPABwCIIfACwCAIfACzifwECgkfJxANCTQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.color_palette(\"icefire_r\")\n",
        "sns.countplot(data=df, x='language_preferred')\n",
        "plt.xlabel('Lenguage Preferred')\n",
        "plt.ylabel('Count');"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "eW_vba2Qo4lg",
      "metadata": {
        "id": "eW_vba2Qo4lg"
      },
      "source": [
        "#### Observations:\n",
        "- The two most preferred languages are French and Spanish. \n",
        "- The graph shows there is a small difference between the user's language preference (English, Spanish and French)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "zZB5Wd5k85eA",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zZB5Wd5k85eA",
        "outputId": "e726321f-71c1-45fa-9223-525bdc6e4b77"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "old    50\n",
              "new    50\n",
              "Name: landing_page, dtype: int64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['landing_page'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "YQMGt-popxge",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "YQMGt-popxge",
        "outputId": "43921257-93ec-4196-e3e2-9b2784b96af8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.color_palette(\"icefire_r\")\n",
        "sns.countplot(data=df, x='landing_page')\n",
        "plt.xlabel('landing Page')\n",
        "plt.ylabel('Count');"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "lSKlJlv0qGo0",
      "metadata": {
        "id": "lSKlJlv0qGo0"
      },
      "source": [
        "### Obsevations:\n",
        "- There is an equal quantity of users on the old and the new landing page. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "1v0ZUHgDgGle",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "1v0ZUHgDgGle",
        "outputId": "35982e57-1f5f-4c40-d167-582c91ffb4f1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.color_palette(\"icefire_r\")\n",
        "df['converted'].value_counts()\n",
        "sns.histplot(data=df, x='converted');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "cExff3W_9d5B",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cExff3W_9d5B",
        "outputId": "f767d718-5da8-4ef4-9f46-b5620723a201"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "yes    54\n",
              "no     46\n",
              "Name: converted, dtype: int64"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['converted'].value_counts()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "a9HlsL_iyEml",
      "metadata": {
        "id": "a9HlsL_iyEml"
      },
      "source": [
        "### Obsevations:\n",
        "- Overall 54 users  of 100 were converted into subscribers."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "fad2de2f",
      "metadata": {
        "id": "fad2de2f"
      },
      "source": [
        "### Bivariate Analysis"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "HcAdHYcU-oaV",
      "metadata": {
        "id": "HcAdHYcU-oaV"
      },
      "source": [
        "#### Converted vs Time spent on the page"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "eqdE6uxuCkwA",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "eqdE6uxuCkwA",
        "outputId": "772fe10c-2b7a-471a-9cd9-85dc051c871d"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.color_palette(\"icefire_r\")\n",
        "sns.barplot(data=df, x='converted',y='time_spent_on_the_page');\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "58e9a1d0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "58e9a1d0",
        "outputId": "7361c344-ba6c-4327-a3b5-ac4a83f2438a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.color_palette(\"icefire_r\")\n",
        "sns.boxplot(data=df, x='converted',y='time_spent_on_the_page')\n",
        "plt.title('Converted vs Time spent on the page')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "E2pslIYTybLH",
      "metadata": {
        "id": "E2pslIYTybLH"
      },
      "source": [
        "### Obsevations:\n",
        "- The range of time spent on the page for people converted lies between 3 and 10 min.\n",
        "- While 75% of the not converted users spent less than 5 min on the page. \n",
        "- This leads us to infer the more time people spend on the page the more likely they are to subscribe."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "4o1FGCz2SbMa",
      "metadata": {
        "id": "4o1FGCz2SbMa"
      },
      "source": [
        "#### Group Vs Time Spent"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "U2z1_yrgCz33",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "U2z1_yrgCz33",
        "outputId": "02111094-6f78-481e-8321-6f9be06f747e"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.color_palette(\"icefire_r\")\n",
        "sns.boxplot(data=df, x='group',y='time_spent_on_the_page');"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "eAS_qVEiSgth",
      "metadata": {
        "id": "eAS_qVEiSgth"
      },
      "source": [
        "#### Landing Page Vs Time Spent"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "anSqHGl9CsYZ",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "anSqHGl9CsYZ",
        "outputId": "ae7baab5-7a5e-4da0-828e-6c02932fd763"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.color_palette(\"icefire_r\")\n",
        "sns.boxplot(data=df, x='landing_page',y='time_spent_on_the_page');"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "Ya5_B9UrFAnI",
      "metadata": {
        "id": "Ya5_B9UrFAnI"
      },
      "source": [
        "### Observations:\n",
        "\n",
        "- Users on the new landing page(Group Treatment) spent more time than users on the old landing page(Group Control).\n",
        "- Users on the old landing page have a lower and dispersed range of time spent. With a min of 20 seconds and 75% of the users spent less than 7.5 min and max of 10.3 min.\n",
        " - while user's time on the new landing page is higher and less dispersed, users on the new landing page spent a nearly a minimum of 3 minutes and max of 10.7 minutes.\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "zu1kRD_lS6Ku",
      "metadata": {
        "id": "zu1kRD_lS6Ku"
      },
      "source": [
        "#### Time spent vs Landing page vs Converted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "Z_MSsYCjCu_3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "Z_MSsYCjCu_3",
        "outputId": "1d6aacaf-7d32-4a57-e21d-52ce5b399977"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.color_palette(\"icefire_r\")\n",
        "sns.boxplot(data=df, x='landing_page',y='time_spent_on_the_page',hue='converted');"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "aZxIRJM0OENG",
      "metadata": {
        "id": "aZxIRJM0OENG"
      },
      "source": [
        "### Observations:\n",
        "- We can see that users that became subscribers spent more time on the websites in both of the landing pages(new and old).\n",
        "- Also users on the new website spent more time than users on the old one."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "pX_xYywLC2rI",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "pX_xYywLC2rI",
        "outputId": "a309abdb-42e1-4b8c-902f-f0c2f5c14959"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.color_palette(\"icefire_r\")\n",
        "sns.boxplot(data=df, x='language_preferred',y='time_spent_on_the_page');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "nLKcceQGC5HO",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "nLKcceQGC5HO",
        "outputId": "5ff3b5da-f80b-4bd5-da76-795250ebda05"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.color_palette(\"icefire_r\")\n",
        "sns.barplot(data=df, x='language_preferred',y='time_spent_on_the_page');"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "tuSmnKvLTUX0",
      "metadata": {
        "id": "tuSmnKvLTUX0"
      },
      "source": [
        "### Observations:\n",
        "\n",
        "- Average time for Spanish, English and French are pretty close to each other, no more than a min of difference.\n",
        "- English appears to have the highest mean time followed by Spanish and then French has the lowest mean time. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "pJHqqngRThMX",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "pJHqqngRThMX",
        "outputId": "a1065853-fb02-4e19-d880-0117de60b2c4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 360x216 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1148.5x360 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize = (5,3));\n",
        "sns.catplot(hue = 'landing_page', data = df, kind = 'count', col = 'language_preferred', x = 'converted');\n",
        "plt.show();\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "1uoxOJvfT_H8",
      "metadata": {
        "id": "1uoxOJvfT_H8"
      },
      "source": [
        "### Observations:\n",
        "- Overall, French has the higher number of rejections with 14 users, but also the highest conversion on the new landing page, 12 users became subscriptors.\n",
        "-Users that prefer English have almost the same quantity of conversions in old(11 users) and new landing(10 users).\n",
        "-Users that prefer Spanish have a large number of conversions on the new landing page, while the users on the old landing page tend to be higher in rejections.\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "27906576",
      "metadata": {
        "id": "27906576"
      },
      "source": [
        "## 1. Do the users spend more time on the new landing page than the existing landing page?"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "e5fdf0c8",
      "metadata": {
        "id": "e5fdf0c8"
      },
      "source": [
        "### Perform Visual Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "6eZJa41eg21n",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 886
        },
        "id": "6eZJa41eg21n",
        "outputId": "4c59f3d3-d30b-4faf-fcbc-3e31fa4affdb"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.color_palette(\"icefire_r\")\n",
        "sns.boxplot(data=df, x='landing_page',y='time_spent_on_the_page');\n",
        "plt.show()\n",
        "sns.barplot(data=df, x='landing_page',y='time_spent_on_the_page');\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "56b3dafd",
      "metadata": {
        "id": "56b3dafd"
      },
      "source": [
        "### Step 1: Define the null and alternate hypotheses\n",
        "\n",
        "\n",
        "*   𝜇1= Time spent on the new landing page\n",
        "*   𝜇2= Time spent on the old landing page\n",
        "\n",
        "\n",
        "\n",
        "Null hypothesis\n",
        "\n",
        "𝐻0:𝜇1<=𝜇2\n",
        "\n",
        "Alternative hypothesis\n",
        "\n",
        "𝐻a:𝜇1>𝜇2"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "c7ee4907",
      "metadata": {
        "id": "c7ee4907"
      },
      "source": [
        "### Step 2: Select Appropriate test\n",
        "Two Independent Sample T-test "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "3f58c9a7",
      "metadata": {
        "id": "3f58c9a7"
      },
      "source": [
        "### Step 3: Decide the significance level\n",
        "α=0.05 "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "5fed2eac",
      "metadata": {
        "id": "5fed2eac"
      },
      "source": [
        "### Step 4: Collect and prepare data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "QsYMGg2GdsT7",
      "metadata": {
        "id": "QsYMGg2GdsT7"
      },
      "outputs": [],
      "source": [
        "new_landing= df[df['landing_page'] == 'new'].reset_index()\n",
        "old_landing=df[df['landing_page'] == 'old'].reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "QUnWDZ70do_s",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUnWDZ70do_s",
        "outputId": "2c51ade3-cdce-4642-92e4-27ab36423d4f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The mean spent time on the new landing page is 6.2232\n",
            "The mean spent time on the old landing page is 4.532400000000001\n",
            "The standard deviation of the new landing page is 1.82\n",
            "The standard deviation of the old landing page is 2.58\n"
          ]
        }
      ],
      "source": [
        "# find the sample means and sample standard deviations for the two samples\n",
        "print('The mean spent time on the new landing page is ' + str(new_landing['time_spent_on_the_page'].mean()))\n",
        "print('The mean spent time on the old landing page is ' + str(old_landing['time_spent_on_the_page'].mean()))\n",
        "print('The standard deviation of the new landing page is ' + str(round(new_landing['time_spent_on_the_page'].std(), 2)))\n",
        "print('The standard deviation of the old landing page is ' + str(round(old_landing['time_spent_on_the_page'].std(), 2)))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "2c183cd7",
      "metadata": {
        "id": "2c183cd7"
      },
      "source": [
        "### Two Independent Sample T-test assumptions are satisfied or not\n",
        "- Continuous data = Yes, time is mesured on a continuous scale.\n",
        "\n",
        "- Normally distributed populations = Yes\n",
        "\n",
        "- Independent populations = We are informed that the two samples are independent from each other.\n",
        "\n",
        "- Unequal population standard deviations = yes.\n",
        "\n",
        "- Random sampling from the population = Yes, we are informed that the collected sample is a simple random sample.\n",
        "\n",
        "- We can use the Two Independent Sample T-test"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "f5c5258d",
      "metadata": {
        "id": "f5c5258d"
      },
      "source": [
        "### Step 5: Calculate the p-value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "fac8594a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fac8594a",
        "outputId": "f9ddefd1-ef51-4195-8fbe-b68f19ce07cf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The p-value is  0.0001392381225166549\n"
          ]
        }
      ],
      "source": [
        "from scipy.stats import ttest_ind\n",
        "test_stat, p_value = ttest_ind(new_landing['time_spent_on_the_page'], old_landing['time_spent_on_the_page'], \n",
        "                               equal_var = False, alternative = 'greater')\n",
        "print('The p-value is ', p_value)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "359b12f8",
      "metadata": {
        "id": "359b12f8"
      },
      "source": [
        "### Step 6: Compare the p-value with $\\alpha$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "7be47289",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7be47289",
        "outputId": "265f57f5-121c-4255-9de6-d1c554f754b9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "As the p-value 0.0001392381225166549 is less than the level of significance, we reject the null hypothesis.\n"
          ]
        }
      ],
      "source": [
        "if p_value < 0.05:\n",
        "    print(f'As the p-value {p_value} is less than the level of significance, we reject the null hypothesis.')\n",
        "else:\n",
        "    print(f'As the p-value {p_value} is greater than the level of significance, we fail to reject the null hypothesis.')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "d9affc84",
      "metadata": {
        "id": "d9affc84"
      },
      "source": [
        "### Step 7:  Draw inference\n",
        "\n",
        "- Based on the sample and our p-value(0.000139), which is much less than the level of significance we can reject the null hypothesis.\n",
        "- Hence, we have enough evidence to support the claim that the mean time spent on the new landing page is more than the mean time spent on the old landing page."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "353e9d24",
      "metadata": {
        "id": "353e9d24"
      },
      "source": [
        "## 2. Is the conversion rate (the proportion of users who visit the landing page and get converted) for the new page greater than the conversion rate for the old page?"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "wYpLlJCxv2bI",
      "metadata": {
        "id": "wYpLlJCxv2bI"
      },
      "source": [
        "### Perform Visual Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "Bq62H7oWOgyH",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "Bq62H7oWOgyH",
        "outputId": "243e3da2-19b9-4e9f-dc66-7048329b880a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "sns.countplot(data=df, x='landing_page',hue='converted');\n",
        "plt.show()\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "JYM_W9f0MPLQ",
      "metadata": {
        "id": "JYM_W9f0MPLQ"
      },
      "source": [
        "### Step 1: Define the null and alternate hypotheses\n",
        "\n",
        "\n",
        "p1= Proportion of users that visit the new landing, \n",
        "\n",
        "p2= Proportion of users that visit the old landing \n",
        "\n",
        "Null hypothesis\n",
        "*   H0: p1 = p2\n",
        "\n",
        "Alternative hypothesis\n",
        "*   Ha: p1 > p2"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "JdXNAnZexEhD",
      "metadata": {
        "id": "JdXNAnZexEhD"
      },
      "source": [
        "### Step 2: Select Appropriate test\n",
        "2-propotion Z-test since we are comparing the sample proportions from two populations."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "1pw0gKXu0GIC",
      "metadata": {
        "id": "1pw0gKXu0GIC"
      },
      "source": [
        "### Step 3: Decide the significance level\n",
        "α=0.05 "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "z40lR7fu09IY",
      "metadata": {
        "id": "z40lR7fu09IY"
      },
      "source": [
        "### Step 4: Colect and prepare the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "oVdvIe1s1Fqd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oVdvIe1s1Fqd",
        "outputId": "b0d0cec0-0b92-4c33-dcfa-62a75eb2cb2e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "P1: 33\n",
            "P2: 21\n",
            "Proportion converted for new landing page: 0.66\n",
            "Proportion converted for the old landing page: 0.42\n"
          ]
        }
      ],
      "source": [
        "convertion_new=df[(df['landing_page'] == 'new')&(df['converted']=='yes')].reset_index()\n",
        "convertion_old=df[(df['landing_page'] == 'old')&(df['converted']=='yes')].reset_index()\n",
        "print('P1:',convertion_new.shape[0])\n",
        "print('P2:',convertion_old.shape[0])\n",
        "print('Proportion converted for new landing page:', convertion_new.shape[0]/new_landing.shape[0])\n",
        "print('Proportion converted for the old landing page:',convertion_old.shape[0]/old_landing.shape[0])"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "sPIOeJdkwr-_",
      "metadata": {
        "id": "sPIOeJdkwr-_"
      },
      "source": [
        "### Two propotion Z-test assumptions are satisfied or not- Binomially distributed populations\n",
        "- Binomially distributed populations: yes, its either converted or not.\n",
        "- Independent populations; yes\n",
        "- Random sampling from the populations: yes\n",
        "- When both mean (np) and\n",
        "n(1-p) are greater than or equal to 10, the binomial distribution can be approximated by a normal distribution: yes\n",
        "\n",
        ">$np_1 = 50\\cdot \\frac{33}{50} =33 \\geq 10\\\\  \n",
        "n(1-p_1) = 50 \\cdot \\frac{50-33}{50} =17 \\geq 10 \\\\    \n",
        "np_2 = 50\\cdot \\frac{21}{50} =21 \\geq 10\\\\ \n",
        "n(1-p_2) = 50 \\cdot \\frac{50-21}{50} =29 \\geq 10 $ "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "7WPQWyjTAZDt",
      "metadata": {
        "id": "7WPQWyjTAZDt"
      },
      "source": [
        "### Step 5: Calculate the p-value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "EpNek-k1_dgQ",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EpNek-k1_dgQ",
        "outputId": "ce7f201f-43bf-410e-e481-1fa63cbacfb0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "p-value :  0.008026308204056278\n"
          ]
        }
      ],
      "source": [
        "from statsmodels.stats.proportion import proportions_ztest\n",
        "conv_count = np.array([33,21]) # converted users in new and old landing pages\n",
        "nobs = np.array([50,50])       #  number of observations in new and old landing pages\n",
        "\n",
        "# this test returns 2 values, test statistic and p-value\n",
        "test_stat, p_value = proportions_ztest(conv_count, nobs, alternative = 'larger') # Ha as larger\n",
        "print('p-value : ', p_value)\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "UJhoPyNLAmZk",
      "metadata": {
        "id": "UJhoPyNLAmZk"
      },
      "source": [
        "### Step 6: Compare the p-value with $\\alpha$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "kPPQSwgcAfVx",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kPPQSwgcAfVx",
        "outputId": "6d29fff6-9f8c-48f4-9c59-e031e5cab282"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "As the p-value 0.008026308204056278 is less than the level of significance, we reject the null hypothesis.\n"
          ]
        }
      ],
      "source": [
        "if p_value < 0.05:\n",
        "    print(f'As the p-value {p_value} is less than the level of significance, we reject the null hypothesis.')\n",
        "else:\n",
        "    print(f'As the p-value {p_value} is greater than the level of significance, we fail to reject the null hypothesis.')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "c53f6uTqGYb9",
      "metadata": {
        "id": "c53f6uTqGYb9"
      },
      "source": [
        "### Step 7:  Draw inference\n",
        "- As the p-value is less(0.008026) than the significance level (0.05), we can reject the null hypothesis.\n",
        "- Thus, we have enough evidence to conclude that the proportion of conversion rate for the new page is greater than the proportion of conversion rate for the old page."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "d49bfa2d",
      "metadata": {
        "id": "d49bfa2d"
      },
      "source": [
        "## 3. Is the conversion and preferred language are independent or related?"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "VuRYRtAZHx3b",
      "metadata": {
        "id": "VuRYRtAZHx3b"
      },
      "source": [
        "### Perform Visual Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "Mt5syZ_BIJQh",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Mt5syZ_BIJQh",
        "outputId": "6d6a70f0-2f0a-4c97-ca55-867e47f33867"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "sns.countplot(hue = 'language_preferred', data = data, x = 'converted')\n",
        "plt.title('Language preferred vs Converted')\n",
        "plt.legend(title = 'Language')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "vE4GbAHlOD3N",
      "metadata": {
        "id": "vE4GbAHlOD3N"
      },
      "source": [
        "### Step 1: Define the null and alternate hypotheses\n",
        "\n",
        "Ho : Converted status is independent of language preferred\n",
        "\n",
        "Ha : Converted status depends on language preferred"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "XjHm3-GxFzCN",
      "metadata": {
        "id": "XjHm3-GxFzCN"
      },
      "source": [
        "### Step 2: Select Appropriate test\n",
        "Chi-Square Test of Independence"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "cabpqvSGF5o3",
      "metadata": {
        "id": "cabpqvSGF5o3"
      },
      "source": [
        "### Step 3: Decide the significance level\n",
        "α=0.05 "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "ThS8R3qvGCKt",
      "metadata": {
        "id": "ThS8R3qvGCKt"
      },
      "source": [
        "### Step 4: Colect and prepare the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "BZREvve3G-Fl",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "BZREvve3G-Fl",
        "outputId": "b1baa21c-9bfd-43d7-c1ab-3f121c21653a"
      },
      "outputs": [
        {
          "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>language_preferred</th>\n",
              "      <th>English</th>\n",
              "      <th>French</th>\n",
              "      <th>Spanish</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>converted</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>no</th>\n",
              "      <td>11</td>\n",
              "      <td>19</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "language_preferred  English  French  Spanish\n",
              "converted                                   \n",
              "no                       11      19       16\n",
              "yes                      21      15       18"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lang_conv = pd.crosstab(df['converted'], df['language_preferred'])\n",
        "lang_conv"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "ZCB_qVblHVtT",
      "metadata": {
        "id": "ZCB_qVblHVtT"
      },
      "source": [
        "### Satisfied Chi-Square Test of Independence Assumptions:\n",
        "- Categorical variables: Yes\n",
        "\n",
        "- Expected value of the number of sample observations in each level of the variable is at least 5: Yes\n",
        "\n",
        "- Random sampling from the population: Yes"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "qIGhmoyeGHb8",
      "metadata": {
        "id": "qIGhmoyeGHb8"
      },
      "source": [
        "### Step 5: Calculate the p-value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "lSfTu6v_Jjc8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lSfTu6v_Jjc8",
        "outputId": "a6b2f3b1-e960-4e96-8fee-58372d523aec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The p-value is 0.21298887487543447\n"
          ]
        }
      ],
      "source": [
        "from scipy.stats import chi2_contingency\n",
        "\n",
        "# find the p-value\n",
        "chi, p_value, dof, expected = chi2_contingency(lang_conv)\n",
        "print('The p-value is', p_value)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "uqvOU3sDGQOs",
      "metadata": {
        "id": "uqvOU3sDGQOs"
      },
      "source": [
        "### Step 6: Compare the p-value with $\\alpha$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "lHwfgW4vKMXy",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lHwfgW4vKMXy",
        "outputId": "d25caf18-d31c-4bab-eb5c-42771b516533"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "As the p-value 0.21298887487543447 is greater than the level of significance, we fail to reject the null hypothesis.\n"
          ]
        }
      ],
      "source": [
        "if p_value < 0.05:\n",
        "    print(f'As the p-value {p_value} is less than the level of significance, we reject the null hypothesis.')\n",
        "else:\n",
        "    print(f'As the p-value {p_value} is greater than the level of significance, we fail to reject the null hypothesis.')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "aUv0vYBjGaXI",
      "metadata": {
        "id": "aUv0vYBjGaXI"
      },
      "source": [
        "### Step 7:  Draw inference\n",
        "\n",
        "- As the p-value (0.213) is greater than the level of significance (0.05), we fail to reject the null hypothesis.\n",
        "- Hence, we do not have enough statistical evidence to conclude that the conversion status depends on language preference."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "8d585a90",
      "metadata": {
        "id": "8d585a90"
      },
      "source": [
        "## 4. Is the time spent on the new page same for the different language users?"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "4Ij4Fky_H0KR",
      "metadata": {
        "id": "4Ij4Fky_H0KR"
      },
      "source": [
        "### Perform Visual Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "Qef9YSeEYSV6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qef9YSeEYSV6",
        "outputId": "aeb9eacb-8066-4250-eef8-24b0fd070531"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   time_spent_on_the_page\n",
            "                                     mean\n",
            "language_preferred                       \n",
            "English                          6.663750\n",
            "French                           6.196471\n",
            "Spanish                          5.835294\n",
            "                   time_spent_on_the_page\n",
            "                                     mean\n",
            "language_preferred                       \n",
            "English                          5.559062\n",
            "French                           5.253235\n",
            "Spanish                          5.331765\n"
          ]
        }
      ],
      "source": [
        "new=df[(df['landing_page']=='new')]\n",
        "new\n",
        "time=new.groupby('language_preferred').agg({'time_spent_on_the_page':['mean']})\n",
        "print(time)\n",
        "print(df.groupby('language_preferred').agg({'time_spent_on_the_page':['mean']}))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "8LHxAVYYLxKV",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "8LHxAVYYLxKV",
        "outputId": "50797c5f-e106-4a19-f23b-a64a4546150a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,5))\n",
        "sns.boxplot(data=new_landing, x='language_preferred', y='time_spent_on_the_page')\n",
        "plt.title('Time spent vs Language preferred in new landing');"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "8_r5dU7jA5TI",
      "metadata": {
        "id": "8_r5dU7jA5TI"
      },
      "source": [
        "### Step 1: Define the null and alternate hypotheses\n",
        "\n",
        "- Null hypothesis\n",
        "\n",
        "Ho : All the mean time spent are equal for each language\n",
        "\n",
        "- Alternative hypothesis\n",
        "\n",
        "Ha : Atleast one of the mean time spent is different from the rest"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "b8vBtrtyFxD6",
      "metadata": {
        "id": "b8vBtrtyFxD6"
      },
      "source": [
        "### Step 2: Select Appropriate test\n",
        "One-way ANOVA test because we are trying to determine if the means of more than two independent populations are significantly different."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "DRa-zIZBF3jA",
      "metadata": {
        "id": "DRa-zIZBF3jA"
      },
      "source": [
        "### Step 3: Decide the significance level\n",
        "α=0.05 "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "0jXuHRYxGD1-",
      "metadata": {
        "id": "0jXuHRYxGD1-"
      },
      "source": [
        "### Step 4: Colect and prepare the data"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "_uylcuxNSdSB",
      "metadata": {
        "id": "_uylcuxNSdSB"
      },
      "source": [
        "### Shapiro-Wilk’s test\n",
        "- Null hypothesis\n",
        "\n",
        "𝐻0:  Time spent follows a normal distribution \n",
        "\n",
        "- Alternative hypothesis\n",
        "\n",
        "𝐻𝑎:  Time spent does not follow a normal distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "lXt4mfDWTPh8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXt4mfDWTPh8",
        "outputId": "a47d26a1-b7e4-4bc5-8f1d-7b5ab9714590"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The p-value is 0.8040016293525696\n"
          ]
        }
      ],
      "source": [
        "# Assumption 1: Normal Distribution\n",
        "from scipy import stats\n",
        "\n",
        "# find the p-value\n",
        "w, p_value = stats.shapiro(new_landing['time_spent_on_the_page']) \n",
        "print('The p-value is', p_value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "Yv4ovM6lUnw0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yv4ovM6lUnw0",
        "outputId": "cdd960b7-cb43-446d-c2d2-ca1b033cfe41"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "As the p-value 0.8040016293525696 is greater than the level of significance, we fail to reject the null hypothesis.\n"
          ]
        }
      ],
      "source": [
        "if p_value < 0.05:\n",
        "    print(f'As the p-value {p_value} is less than the level of significance, we reject the null hypothesis.')\n",
        "else:\n",
        "    print(f'As the p-value {p_value} is greater than the level of significance, we fail to reject the null hypothesis.')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "g8Rf_GqEUzfW",
      "metadata": {
        "id": "g8Rf_GqEUzfW"
      },
      "source": [
        "### Levene's Test\n",
        "- Testing the null hypothesis\n",
        "\n",
        "Ho : All the population variances are equal\n",
        "\n",
        "- Alternative hypothesis\n",
        "\n",
        "Ha : Atleast one variance is different from the rest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "r_OKBuOiVA8P",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_OKBuOiVA8P",
        "outputId": "7a77ab67-e7e8-4565-c4de-72716223e203"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "p-value :  0.46711357711340173\n"
          ]
        }
      ],
      "source": [
        "#Assumption 2: Homogeneity of Variance\n",
        "from scipy.stats import levene\n",
        "\n",
        "statistic, p_value = levene(new_landing['time_spent_on_the_page'][new_landing['language_preferred']=='English'],\n",
        "                            new_landing['time_spent_on_the_page'][new_landing['language_preferred']== 'French'],\n",
        "                            new_landing['time_spent_on_the_page'][new_landing['language_preferred']=='Spanish'])\n",
        "print('p-value : ', p_value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "PgBFYBfsWhsg",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PgBFYBfsWhsg",
        "outputId": "16106c64-b4bb-48e6-cf28-bae739b61e9e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "As the p-value 0.46711357711340173 is greater than the level of significance, we fail to reject the null hypothesis.\n"
          ]
        }
      ],
      "source": [
        "if p_value < 0.05:\n",
        "    print(f'As the p-value {p_value} is less than the level of significance, we reject the null hypothesis.')\n",
        "else:\n",
        "    print(f'As the p-value {p_value} is greater than the level of significance, we fail to reject the null hypothesis.')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "Mj1LpkgBNzC8",
      "metadata": {
        "id": "Mj1LpkgBNzC8"
      },
      "source": [
        "### Satisfied One-way ANOVA Test of Independence Assumptions:\n",
        "\n",
        "\n",
        "- The populations are normally distributed - Yes, the normality assumption is verified using the Shapiro-Wilk’s test.\n",
        "- Samples are independent simple random samples - Yes, we are informed that the collected sample is a simple random sample.\n",
        "- Population variances are equal - Yes, the homogeneity of variance assumption is verified using the Levene's test."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "mbD4gStaGJHP",
      "metadata": {
        "id": "mbD4gStaGJHP"
      },
      "source": [
        "### Step 5: Calculate the p-value"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "0oRzMdT2XLYO",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0oRzMdT2XLYO",
        "outputId": "ae2ee7b7-adbc-4352-dbed-44759868b3b2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "p-value :  0.43204138694325955\n"
          ]
        }
      ],
      "source": [
        "#import the required function\n",
        "from scipy.stats import f_oneway\n",
        "\n",
        "# perform one-way anova test\n",
        "test_stat, p_value = f_oneway(new_landing.loc[new_landing['language_preferred']=='English','time_spent_on_the_page'],\n",
        "                              new_landing.loc[new_landing['language_preferred']=='French','time_spent_on_the_page'],\n",
        "                              new_landing.loc[new_landing['language_preferred']=='Spanish','time_spent_on_the_page'])\n",
        "print('p-value : ', p_value)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "E0NT51WKGOo4",
      "metadata": {
        "id": "E0NT51WKGOo4"
      },
      "source": [
        "### Step 6: Compare the p-value with $\\alpha$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "woKLM2i8XKXr",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "woKLM2i8XKXr",
        "outputId": "c88de904-a5b7-4297-b4e8-2c9abb6c1c08"
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          "text": [
            "As the p-value 0.43204138694325955 is greater than the level of significance, we fail to reject the null hypothesis.\n"
          ]
        }
      ],
      "source": [
        "if p_value < 0.05:\n",
        "    print(f'As the p-value {p_value} is less than the level of significance, we reject the null hypothesis.')\n",
        "else:\n",
        "    print(f'As the p-value {p_value} is greater than the level of significance, we fail to reject the null hypothesis.')"
      ]
    },
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        "### Step 7:  Draw inference"
      ]
    },
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        "- The p-value (0.4320) is greater than the significance level(0.05), so we fail to reject the null hypothesis.\n",
        "\n",
        "- There is not enough statistcal evidence to conclude that the average time spent on the different landing pages differed by any significant amount based on the user's language (Spanish, English, and French)."
      ]
    },
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      "source": [
        "## Conclusion and Business Recommendations"
      ]
    },
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        "- From the visual and statistical analysis we can conclude about the research:\n",
        "\n",
        "\n",
        "   - The average time spent by users on the new landing page is higher than the old one.\n",
        "   - The Conversion rate is higher for the new landing page.\n",
        "   - The Conversion status does not depend on the language preference.\n",
        "   - The mean time spent on the new landing page does not differ by a significant level based on the users' language(Spanish, English and French).\n",
        "  \n",
        "- Based on these findings we can support that the decision of switching to the new landing page would have a positive impact in engagement and conversion rates for the business.\n",
        "\n",
        "\n",
        "- It is important to keep monitoring the performance of the new landing page over time to ensure that the positive trend and behavior continues.\n",
        "\n",
        "\n",
        "- If in the future, the idea of optimizing the landing page becomes a need, consider conducting additional tests to develop the idea. You can test with different content, copy, layouts, images, or more elements to evaluate if they have an impact on conversion rates or engagement metrics.\n",
        "\n",
        "\n",
        "- Always keep trying different ways to personalize the user experience based on language preference. While language preference may not have a direct impact on conversion rates right now, language is related to culture and when you provide content in the user's preferred language, it may have a positive impact that can help improve engagement and satisfaction overall.\n",
        "\n",
        "\n",
        "- Based on the success on the new landing page you can explore opportunities to expand into new markets or target new customers. This is worth investing in additional marketing efforts to reach new audiences.\n",
        "\n",
        "\n",
        "- The business should continue to focus on optimizing the user experience to drive even better results.\n",
        "\n"
      ]
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      "source": [
        "___"
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