ENews_Full_Code_MarinaGrimal.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6yDxo9u0d9Na"
      },
      "source": [
        "# Project Business Statistics: E-news Express"
      ],
      "id": "6yDxo9u0d9Na"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Context"
      ],
      "metadata": {
        "id": "fvj2RBTMhV9N"
      },
      "id": "fvj2RBTMhV9N"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SrLd-ICEmPSD"
      },
      "source": [
        "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."
      ],
      "id": "SrLd-ICEmPSD"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Objective"
      ],
      "metadata": {
        "id": "JcdFrYfshf1r"
      },
      "id": "JcdFrYfshf1r"
    },
    {
      "cell_type": "markdown",
      "source": [
        "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",
        "- Do the users spend more time on the new landing page than on the existing landing page?\n",
        "\n",
        "- 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",
        "\n",
        "- Does the converted status depend on the preferred language?\n",
        "\n",
        "- Is the time spent on the new page the same for the different language users?"
      ],
      "metadata": {
        "id": "Gmt84U91heYQ"
      },
      "id": "Gmt84U91heYQ"
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    {
      "cell_type": "markdown",
      "source": [
        "## Data Dictionary"
      ],
      "metadata": {
        "id": "C7OyZrGChjWa"
      },
      "id": "C7OyZrGChjWa"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Vt1PLLqmXNn"
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      "source": [
        "The data contains information regarding the interaction of users in both groups with the two versions of the landing page.\n",
        "\n",
        "- user_id - Unique user ID of the person visiting the website\n",
        "\n",
        "- group - Whether the user belongs to the first group (control) or the second group (treatment)\n",
        "\n",
        "- landing_page - Whether the landing page is new or old\n",
        "\n",
        "- time_spent_on_the_page - Time (in minutes) spent by the user on the landing page\n",
        "\n",
        "- converted - Whether the user gets converted to a subscriber of the news portal or not\n",
        "\n",
        "- language_preferred - Language chosen by the user to view the landing page"
      ],
      "id": "9Vt1PLLqmXNn"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6a3d218"
      },
      "source": [
        "## Import all the necessary libraries"
      ],
      "id": "a6a3d218"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "658c5dec"
      },
      "source": [
        "# Importing all the necessary libraries for data manipulation and visualization\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns"
      ],
      "id": "658c5dec",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6970dd99"
      },
      "source": [
        "## Reading the Data into a DataFrame"
      ],
      "id": "6970dd99"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d3b95640",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "221b999b-b1ec-43a6-d292-4842e3ca1fbc"
      },
      "source": [
        "# Giving Google Colab access to Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "id": "d3b95640",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading the csv file from Drive into Colab using read.csv function\n",
        "data = pd.read_csv('/content/drive/MyDrive/PG Data Science and Business Analysis/Project 2: E-news Express/abtest.csv')"
      ],
      "metadata": {
        "id": "lPgzN4o64jkA"
      },
      "id": "lPgzN4o64jkA",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9d75cfa2"
      },
      "source": [
        "## Explore the dataset and extract insights using Exploratory Data Analysis"
      ],
      "id": "9d75cfa2"
    },
    {
      "cell_type": "markdown",
      "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"
      ],
      "id": "27201534"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e1ad11d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "19e63b12-e702-4af9-b2b7-65670a3f76f2"
      },
      "source": [
        "# Displaying the first 5 rows of the data\n",
        "data.head()"
      ],
      "id": "e1ad11d4",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "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  "
            ],
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"user_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52,\n        \"min\": 546443,\n        \"max\": 546592,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          546580,\n          546576,\n          546568\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"treatment\",\n          \"control\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"landing_page\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"new\",\n          \"old\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spent_on_the_page\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.378166078933492,\n        \"min\": 0.19,\n        \"max\": 10.71,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          8.46,\n          3.65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"converted\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"language_preferred\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Spanish\",\n          \"English\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Displaying the last 5 rows of the dataset\n",
        "data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HDdYCnVP5NpY",
        "outputId": "33220bce-3139-4fc4-a979-b176513ba4da"
      },
      "id": "HDdYCnVP5NpY",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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  "
            ],
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              "  @keyframes spin {\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"user_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36,\n        \"min\": 546446,\n        \"max\": 546544,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          546544,\n          546483,\n          546472\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"control\",\n          \"treatment\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"landing_page\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"old\",\n          \"new\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spent_on_the_page\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7192704637339141,\n        \"min\": 5.15,\n        \"max\": 7.07,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6.52,\n          5.86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"converted\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"language_preferred\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"English\",\n          \"Spanish\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Looking at the shape of the dataset\n",
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RFtqA8D45ejx",
        "outputId": "499ea089-1471-47a6-ab31-c4fe232ea98d"
      },
      "id": "RFtqA8D45ejx",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for duplicates\n",
        "data.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4O3YmFMi6Tdm",
        "outputId": "61768adf-22c2-48ed-c5bf-ef20d3c8faa7"
      },
      "id": "4O3YmFMi6Tdm",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Printing a summary of the dataset\n",
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JuP3N8pf6QQH",
        "outputId": "4f1f63c1-d43c-4042-fde7-f25b6cb469f9"
      },
      "id": "JuP3N8pf6QQH",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Changing the data type of user_id to a categorical value since Python interpreted it as numerical\n",
        "data['user_id'] = pd.Categorical(data['user_id'])"
      ],
      "metadata": {
        "id": "u3baBEu1BIq9"
      },
      "id": "u3baBEu1BIq9",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking that data type have changed correctly\n",
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UYl5H5QcBSCq",
        "outputId": "66e63728-aed9-43f1-8a34-7a43a95765da"
      },
      "id": "UYl5H5QcBSCq",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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    category\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: category(1), float64(1), object(4)\n",
            "memory usage: 9.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for missing values in the dataset\n",
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CUk-zdp47SHh",
        "outputId": "a88fcc0f-8628-44fe-d22e-c693966a1fcc"
      },
      "id": "CUk-zdp47SHh",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "user_id                   0\n",
              "group                     0\n",
              "landing_page              0\n",
              "time_spent_on_the_page    0\n",
              "converted                 0\n",
              "language_preferred        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Looking at a statistical summary of the data that includes all columns\n",
        "data.describe(include='all')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "KLGmGNRU7kEu",
        "outputId": "589cc1ef-9cf4-4656-93df-a73cf131458f"
      },
      "id": "KLGmGNRU7kEu",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         user_id    group landing_page  time_spent_on_the_page converted  \\\n",
              "count      100.0      100          100              100.000000       100   \n",
              "unique     100.0        2            2                     NaN         2   \n",
              "top     546443.0  control          old                     NaN       yes   \n",
              "freq         1.0       50           50                     NaN        54   \n",
              "mean         NaN      NaN          NaN                5.377800       NaN   \n",
              "std          NaN      NaN          NaN                2.378166       NaN   \n",
              "min          NaN      NaN          NaN                0.190000       NaN   \n",
              "25%          NaN      NaN          NaN                3.880000       NaN   \n",
              "50%          NaN      NaN          NaN                5.415000       NaN   \n",
              "75%          NaN      NaN          NaN                7.022500       NaN   \n",
              "max          NaN      NaN          NaN               10.710000       NaN   \n",
              "\n",
              "       language_preferred  \n",
              "count                 100  \n",
              "unique                  3  \n",
              "top               Spanish  \n",
              "freq                   34  \n",
              "mean                  NaN  \n",
              "std                   NaN  \n",
              "min                   NaN  \n",
              "25%                   NaN  \n",
              "50%                   NaN  \n",
              "75%                   NaN  \n",
              "max                   NaN  "
            ],
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              "  <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>count</th>\n",
              "      <td>100.0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>100.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>546443.0</td>\n",
              "      <td>control</td>\n",
              "      <td>old</td>\n",
              "      <td>NaN</td>\n",
              "      <td>yes</td>\n",
              "      <td>Spanish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1.0</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>NaN</td>\n",
              "      <td>54</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.377800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>2.378166</td>\n",
              "      <td>NaN</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.190000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.880000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.415000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.022500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.710000</td>\n",
              "      <td>NaN</td>\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"user_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 273188.00398626586,\n        \"min\": 1.0,\n        \"max\": 546443.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          100.0,\n          546443.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"50\",\n          \"100\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"landing_page\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"50\",\n          \"100\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spent_on_the_page\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33.733788000034416,\n        \"min\": 0.19,\n        \"max\": 100.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.377800000000001,\n          5.415,\n          100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"converted\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"54\",\n          \"100\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"language_preferred\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"34\",\n          \"100\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking what the 3 languages chosen are\n",
        "data['language_preferred'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "stOV3uLR_Tsp",
        "outputId": "1ba952c4-e411-42b6-d216-95cc7aec3bf5"
      },
      "id": "stOV3uLR_Tsp",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "language_preferred\n",
              "Spanish    34\n",
              "French     34\n",
              "English    32\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* The dataset has been loaded properly. It consists of a 100 rows and 6 columns as described in the Data Dictionary.\n",
        "* Each column has information attributed to the user. User information is provided in rows.\n",
        "* There are no duplicates in the dataset and no missing values as inferred from the summary and verified with isnull() function. All columns have 100 values input.\n",
        "* There are 4 object type columns ('group', 'landing_page', 'converted', 'language_preferred'), 1 numerical/float ('time_spent_on_the_page'), and 1 categorical ('user_id').\n",
        "* There are 50 control users and 50 treatment users. There are 50 users in the new landing page and 50 in the old landing page. As described in the objective, the users in the control group were served the old landing page and the users in the treatment group were served the new landing page.\n",
        "* Spanish and French are the top languages with 34 users each.\n",
        "* 54 users converted to subscribers.\n",
        "* Users spend an average of 5.38 minutes on the landing page. The median is 5.41, since the mean and median are close in value we can infer that it is symmetrical distribution."
      ],
      "metadata": {
        "id": "S0AzbJDl6KfR"
      },
      "id": "S0AzbJDl6KfR"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "68f3b2c9"
      },
      "source": [
        "### Univariate Analysis"
      ],
      "id": "68f3b2c9"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### User_id"
      ],
      "metadata": {
        "id": "RnQzJbOtCPLT"
      },
      "id": "RnQzJbOtCPLT"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1932d0f3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33c33a66-4bff-4378-c177-c97205165c6a"
      },
      "source": [
        "# Checking that there is a singular user_id value for each entry\n",
        "data['user_id'].nunique()"
      ],
      "id": "1932d0f3",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* There are a 100 users in the dataset. User_id is an identifier for the users."
      ],
      "metadata": {
        "id": "9AE59VYpDkFz"
      },
      "id": "9AE59VYpDkFz"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Group"
      ],
      "metadata": {
        "id": "XudYRwtYECuT"
      },
      "id": "XudYRwtYECuT"
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the column group\n",
        "sns.countplot(data=data,x='group');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "g1DnKjPbEtfp",
        "outputId": "73283979-a4f4-4d67-b00a-d1feeda93368"
      },
      "id": "g1DnKjPbEtfp",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the amount of users in each group is the same\n",
        "data['group'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "claaqPXHEU9s",
        "outputId": "3390c9df-84f8-4918-9027-2e842e729b48"
      },
      "id": "claaqPXHEU9s",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "group\n",
              "control      50\n",
              "treatment    50\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* There are 2 groups as defined in the dictionary.\n",
        "* There are 50 users in each group (control and treatment). The distribution is equally distributed."
      ],
      "metadata": {
        "id": "xnMmacyUERY1"
      },
      "id": "xnMmacyUERY1"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Landing page"
      ],
      "metadata": {
        "id": "afE0n4hlF53M"
      },
      "id": "afE0n4hlF53M"
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing landing page\n",
        "sns.countplot(data=data,x='landing_page');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "-6OUuPd6GNwJ",
        "outputId": "5d477092-3589-466c-98b8-d4ef5906e493"
      },
      "id": "-6OUuPd6GNwJ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* There are 2 unique values in landing_page (old and new).\n",
        "* Each page has 50 users. Users in the old page belong to the control group and users in the new page belong to the treatment group.\n"
      ],
      "metadata": {
        "id": "m2FykJW4GmNR"
      },
      "id": "m2FykJW4GmNR"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Time spent on the page in minutes"
      ],
      "metadata": {
        "id": "NqymunB7HHta"
      },
      "id": "NqymunB7HHta"
    },
    {
      "cell_type": "code",
      "source": [
        "# Visuals on numerical data for time spent on the page\n",
        "sns.histplot(data=data,x='time_spent_on_the_page',bins=10,kde=True)\n",
        "plt.show()\n",
        "sns.boxplot(data=data,x='time_spent_on_the_page',showmeans=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 883
        },
        "id": "4ujGgN2vHOKR",
        "outputId": "e32eca7b-2917-4a0c-cdf9-f26f57ff27f5"
      },
      "id": "4ujGgN2vHOKR",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* The mean and median of time spent on the landing page are very close together. This indicates a symmetrical distribution that can be observed on the kde curve.\n",
        "* The mode of the distribution shows that most users spend about 5 minutes on the page.\n",
        "* 50% of the users spend between 4 and 7 minutes on the landing page.\n",
        "* There are no outliers in the data. Few users spend 10 minutes or more on the page."
      ],
      "metadata": {
        "id": "irYgT_PuHsio"
      },
      "id": "irYgT_PuHsio"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Converted"
      ],
      "metadata": {
        "id": "Kr280nqSJj7i"
      },
      "id": "Kr280nqSJj7i"
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing converted users\n",
        "sns.countplot(data=data,x='converted');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "gfvJXHRcKI2Q",
        "outputId": "d4c7c2de-9658-47d8-b11e-fc0af8c03f02"
      },
      "id": "gfvJXHRcKI2Q",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Looking at the percentages of users converted to subscribers\n",
        "data.value_counts('converted',normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vcgxekOqJtbq",
        "outputId": "85d9b041-7a12-42be-d2e2-d807a12de2f2"
      },
      "id": "vcgxekOqJtbq",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "converted\n",
              "yes    0.54\n",
              "no     0.46\n",
              "Name: proportion, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* 54% or 54 users converted to a subscriber.\n",
        "* 46 users out of the 100 didn't subscribe to the news portal."
      ],
      "metadata": {
        "id": "4rhYx-NnK9BO"
      },
      "id": "4rhYx-NnK9BO"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Language preferred"
      ],
      "metadata": {
        "id": "MkVOLCQALy13"
      },
      "id": "MkVOLCQALy13"
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the languages preferred on the pages\n",
        "sns.countplot(data=data,x='language_preferred');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "GoPdfC2dL1Mo",
        "outputId": "70a2fe3d-fd4c-49f7-a006-06520397e028"
      },
      "id": "GoPdfC2dL1Mo",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the amount of users of each language\n",
        "data['language_preferred'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I9t7FyLnMibi",
        "outputId": "b4be1d1a-1753-4fda-982a-2bda3946d7a5"
      },
      "id": "I9t7FyLnMibi",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "language_preferred\n",
              "Spanish    34\n",
              "French     34\n",
              "English    32\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the percentage of users in each language\n",
        "data['language_preferred'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ip25-qkTMsKc",
        "outputId": "010e79b1-35ec-4291-dd18-af64de85788d"
      },
      "id": "ip25-qkTMsKc",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "language_preferred\n",
              "Spanish    0.34\n",
              "French     0.34\n",
              "English    0.32\n",
              "Name: proportion, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* There are 3 unique values for languages (Spanish, English, and French).\n",
        "* Spanish and French both have 34 users comprising 34% of users each.\n",
        "* English has 32 users comprising 32% of users."
      ],
      "metadata": {
        "id": "Oa_niL-dMK6u"
      },
      "id": "Oa_niL-dMK6u"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fad2de2f"
      },
      "source": [
        "### Bivariate Analysis"
      ],
      "id": "fad2de2f"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Landing page and Time spent"
      ],
      "metadata": {
        "id": "0kvKh6RAPqBP"
      },
      "id": "0kvKh6RAPqBP"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58e9a1d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        },
        "outputId": "43fb59bc-a281-49a3-a73d-078c584c136a"
      },
      "source": [
        "# Visual on correlation between time spent on the page and landing page\n",
        "sns.boxplot(data=data,x='landing_page',y='time_spent_on_the_page',showmeans=True)\n",
        "plt.show()\n",
        "g = sns.FacetGrid(data, col=\"landing_page\")\n",
        "g.map(sns.histplot, \"time_spent_on_the_page\",kde=True);"
      ],
      "id": "58e9a1d0",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* On average users spend more time on the new page (about 6 minutes) than the old (a little over 4 minutes).\n",
        "* The old landing page is slightly right skewed. 50% of users in the old page spend between 3 and 6 minutes.\n",
        "* The new landing page has a few outliers on both ends and is almost of symmetrical distribution. 50% of users in this page spend between 5 and 7 minutes."
      ],
      "metadata": {
        "id": "d_4x3711QOCn"
      },
      "id": "d_4x3711QOCn"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Landing page and Converted"
      ],
      "metadata": {
        "id": "28EPX49CTBZ-"
      },
      "id": "28EPX49CTBZ-"
    },
    {
      "cell_type": "code",
      "source": [
        "# Visual on correlation between landing page and converted\n",
        "sns.countplot(data=data,x='landing_page',hue='converted');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "f8I6x-D1TeuH",
        "outputId": "aa16279f-e2e0-42bd-b8d8-1ce64af03016"
      },
      "id": "f8I6x-D1TeuH",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# To create a dataset where only the rows that contain old are included from the landing page column\n",
        "old_page=data[data['landing_page']=='old']\n",
        "\n",
        "# Getting the conversion rate of users in the old landing page\n",
        "old_page['converted'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJTXpIvCUuiI",
        "outputId": "906acdfb-b05a-4d60-bbba-a06ca33fe220"
      },
      "id": "nJTXpIvCUuiI",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "converted\n",
              "no     0.58\n",
              "yes    0.42\n",
              "Name: proportion, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# To create a dataset where only the rows that contain new are included from from the landing page column\n",
        "new_page=data[data['landing_page']=='new']\n",
        "\n",
        "# Getting the conversion percentage of users in the new landing page\n",
        "new_page['converted'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xPg7eDQaWkXB",
        "outputId": "be865cc1-e92f-4e0e-e5ff-8f8183edd3de"
      },
      "id": "xPg7eDQaWkXB",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "converted\n",
              "yes    0.66\n",
              "no     0.34\n",
              "Name: proportion, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* More users from the new landing page subscribe to the news website.\n",
        "* Users in the old landing page are less likely to subscribe.\n",
        "* 66% of users in the new landing page subscribed, while only 42% of users in the old landing page subscribed."
      ],
      "metadata": {
        "id": "o0quuhClXVKY"
      },
      "id": "o0quuhClXVKY"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Converted and Time spent"
      ],
      "metadata": {
        "id": "ZViCeykiX7wm"
      },
      "id": "ZViCeykiX7wm"
    },
    {
      "cell_type": "code",
      "source": [
        "# Visual on correlation between converted users and time spent on the landing page\n",
        "sns.boxplot(data=data,x='converted',y='time_spent_on_the_page',showmeans=True)\n",
        "plt.show()\n",
        "g = sns.FacetGrid(data, col=\"converted\")\n",
        "g.map(sns.histplot, \"time_spent_on_the_page\",kde=True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "id": "Z8DWhJrVYTQr",
        "outputId": "8c23798f-a4b9-4042-a6c7-c532cbbf3d42"
      },
      "id": "Z8DWhJrVYTQr",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* On average converted users spend about 7 minutes on the page and not converted spend around 4 minutes.\n",
        "* There is an outlier on the upper whisker on not converted users. However, there are a few outliers on both ends of the converted users boxplot.\n",
        "* Converted users spend at least 3 minutes and at most 9 minutes with a few outliers on around 10 minutes.\n",
        "* Not converted users spend between less than one minute and 9 minutes on the page."
      ],
      "metadata": {
        "id": "6x4vpugAZhVA"
      },
      "id": "6x4vpugAZhVA"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Time spent and Language preferred"
      ],
      "metadata": {
        "id": "aaGZRXQAa8Xr"
      },
      "id": "aaGZRXQAa8Xr"
    },
    {
      "cell_type": "code",
      "source": [
        "# Visual on correlation between time spent on the page and language preferred\n",
        "sns.boxplot(data=data,x='language_preferred',y='time_spent_on_the_page',showmeans=True)\n",
        "plt.show()\n",
        "g = sns.FacetGrid(data, col='language_preferred')\n",
        "g.map(sns.histplot, 'time_spent_on_the_page',kde=True,bins=10);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        },
        "id": "E0uzmzABbOu7",
        "outputId": "651c4801-afea-4c5f-9310-a73ca6acaca1"
      },
      "id": "E0uzmzABbOu7",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x300 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* The mean and median of all 3 languages are about 5 to 6 minutes spent on the page.\n",
        "* Spanish users has one outlier on the lower end and is left skewed. Spanish users spend between 2 to 9 minutes on the page. 50% of Spanish users spend between 4 and 7 minutes.\n",
        "* French users spend a bit longer on the page than users of the other languages, but have a slightly lower use average.\n",
        "* English and French users have a similar time spent on the website with English having a slightly higher average time."
      ],
      "metadata": {
        "id": "dC7vJQr1b1Ju"
      },
      "id": "dC7vJQr1b1Ju"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Landing page and Language preferred"
      ],
      "metadata": {
        "id": "mQKw1hoRfOMl"
      },
      "id": "mQKw1hoRfOMl"
    },
    {
      "cell_type": "code",
      "source": [
        "# Visual between landing page and language preferred\n",
        "sns.countplot(data=data, hue='landing_page', x='language_preferred');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "VeSyaJCkfdRI",
        "outputId": "85f428ce-eeb0-4a72-cb4e-ac71e9a801ff"
      },
      "id": "VeSyaJCkfdRI",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* Both the old and new landing pages have the same amount of users of each language preferred."
      ],
      "metadata": {
        "id": "bXL_jbPQfz-L"
      },
      "id": "bXL_jbPQfz-L"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Language preferred and Converted users"
      ],
      "metadata": {
        "id": "BSPrBJTjgozr"
      },
      "id": "BSPrBJTjgozr"
    },
    {
      "cell_type": "code",
      "source": [
        "# Visual between language preferred and landing page\n",
        "sns.countplot(data=data, hue='converted', x='language_preferred');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "9IwHUESOg17j",
        "outputId": "dd7b17a0-8218-42bd-d501-2056a6592b30"
      },
      "id": "9IwHUESOg17j",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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/rJ9//llr166VJGVkZCgqKkqPP/54rnEuLi7y9/dXQkKCNm3apI0bN+of//iHXnvtNW3evFkVKlTINaakEIAAAEChDR06VLNmzdJvv/2mkJAQ+fv7S5JatmyphIQE1alTJ9+xlSpVUvfu3dW9e3eNGDFCDRo00L59+9SyZcvSKp8ABAAACu+pp57S2LFjNX/+fC1e/N/fdJs4caL+53/+RwEBAXriiSfk5OSkvXv3av/+/Zo2bZpiYmKUnZ2toKAgVa5cWUuWLFGlSpVUo0aNUq2fZ4AAAECheXh4qFevXqpSpYrCw8Ot7aGhoVqzZo02bNigNm3a6P7779fMmTOtAcfT01Pz58/XAw88oGbNmmnTpk364osvVK1atVKtnytAAAA4mLLyxYS//fab+vfvL2dnZ5v20NBQhYaG5jkmPDzcJjDZCwEIAAAUyvnz5xUXF6e4uDi988479i6nSAhAAACgUFq0aKHz58/r1VdfVf369e1dTpEQgFDiSvs3bRxVWbmkDQA3c+zYMXuXcNt4CBoAAJgOAQgAAJgOAQgAgFKSI8kwJMmwcyVll2EUz2dn1wD07bffqnv37vLz85PFYtGqVatstlssljyX1157Ld85J0+enKt/gwYNSvhIAAC4ufTLf+iP7BzlXLtq71LKrEuXLknSbf9shl0fgs7MzFRgYKAGDx6c52+GpKSk2Kx/+eWXGjJkiHr16lXgvI0bN9amTZus6+XL86w3AMD+rvyRo+8Sz6prxfLyrCo5VagoyaIrV67YuzSHZxiGLl26pFOnTsnT01PlypW7rfnsmgzCwsIUFhaW73YfHx+b9dWrV6tTp06qXbt2gfOWL18+11gAABzBlwfOSJIevO8PlS/nJItFKnclzb5FlSGenp7F8m98mbk0cvLkSa1du1aLFi26ad8jR47Iz89PLi4uCg4OVnR0tAICAvLtn5WVpaysLOt6enp6sdQMAMCNDEnrDpxR7OFz8qhUXk6SVowLt3NVZUOFChVu+8rPdWUmAC1atEhubm553ir7q6CgIMXExKh+/fpKSUlRVFSUHnroIe3fv19ubm55jomOjlZUVFRJlA0AQJ6y/sjRqYt/Pgvk4uJi52rMp8y8BbZgwQL179//pidJWFiYevfurWbNmik0NFTr1q1TWlqaPvnkk3zHREZG6sKFC9blxIkTxV0+AABwIGXiCtCWLVuUkJCgZcuWFXqsp6en6tWrp6NHj+bbx9nZOdcPuQEAgDtXmbgC9P7776tVq1YKDAws9NiMjAwlJibK19e3BCoDAABlkV0DUEZGhuLj4xUfHy9JSkpKUnx8vJKTk6190tPTtXz5cg0dOjTPObp06aLZs2db18eOHavNmzfr2LFj2rZtm3r27Kly5cqpX79+JXosAACg7LDrLbBdu3apU6dO1vUxY8ZIkiIiIhQTEyNJWrp0qQzDyDfAJCYm6syZM9b1X3/9Vf369dPZs2fl5eWlBx98UDt27JCXl1fJHQgAAChT7BqAOnbseNOvtB4+fLiGDx+e7/Ybf5F26dKlxVEaAAC4g5WJZ4AAAACKEwEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYTnl7F3Ana/XCYnuX4BBWutm7AgAAbHEFCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmI5dA9C3336r7t27y8/PTxaLRatWrbLZPnDgQFksFpulW7duN513zpw5qlmzplxcXBQUFKQffvihhI4AAACURXYNQJmZmQoMDNScOXPy7dOtWzelpKRYl48//rjAOZctW6YxY8Zo0qRJ2rNnjwIDAxUaGqpTp04Vd/kAAKCMKm/PnYeFhSksLKzAPs7OzvLx8bnlOd98800NGzZMgwYNkiTNmzdPa9eu1YIFC/TPf/7ztuoFAAB3Bod/BiguLk7Vq1dX/fr19cwzz+js2bP59r169ap2796tkJAQa5uTk5NCQkK0ffv2fMdlZWUpPT3dZgEAAHcuu14Buplu3brp8ccfV61atZSYmKgXX3xRYWFh2r59u8qVK5er/5kzZ5SdnS1vb2+bdm9vbx06dCjf/URHRysqKqrY6wcA4FYkT2lq7xIcQsDEfaW2L4cOQE8++aT1z02bNlWzZs103333KS4uTl26dCm2/URGRmrMmDHW9fT0dPn7+xfb/AAAwLE4/C2wv6pdu7buvvtuHT16NM/td999t8qVK6eTJ0/atJ88ebLA54icnZ3l7u5uswAAgDtXmQpAv/76q86ePStfX988t1esWFGtWrVSbGystS0nJ0exsbEKDg4urTIBAICDs2sAysjIUHx8vOLj4yVJSUlJio+PV3JysjIyMvTCCy9ox44dOnbsmGJjY9WjRw/VqVNHoaGh1jm6dOmi2bNnW9fHjBmj+fPna9GiRTp48KCeeeYZZWZmWt8KAwAAsOszQLt27VKnTp2s69efw4mIiNDcuXP1008/adGiRUpLS5Ofn58efvhhTZ06Vc7OztYxiYmJOnPmjHW9b9++On36tCZOnKjU1FQ1b95c69evz/VgNAAAMC+7BqCOHTvKMIx8t3/11Vc3nePYsWO52kaOHKmRI0feTmkAAOAOVqaeAQIAACgOBCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6BCAAAGA6dg1A3377rbp37y4/Pz9ZLBatWrXKuu3atWsaP368mjZtKldXV/n5+WnAgAH6/fffC5xz8uTJslgsNkuDBg1K+EgAAEBZYtcAlJmZqcDAQM2ZMyfXtkuXLmnPnj2aMGGC9uzZo88++0wJCQl67LHHbjpv48aNlZKSYl2+++67kigfAACUUeXtufOwsDCFhYXluc3Dw0MbN260aZs9e7batm2r5ORkBQQE5Dtv+fLl5ePjU6y1AgCAO0eZegbowoULslgs8vT0LLDfkSNH5Ofnp9q1a6t///5KTk4usH9WVpbS09NtFgAAcOcqMwHoypUrGj9+vPr16yd3d/d8+wUFBSkmJkbr16/X3LlzlZSUpIceekgXL17Md0x0dLQ8PDysi7+/f0kcAgAAcBBlIgBdu3ZNffr0kWEYmjt3boF9w8LC1Lt3bzVr1kyhoaFat26d0tLS9Mknn+Q7JjIyUhcuXLAuJ06cKO5DAAAADsSuzwDdiuvh5/jx4/r6668LvPqTF09PT9WrV09Hjx7Nt4+zs7OcnZ1vt1QAAFBGOPQVoOvh58iRI9q0aZOqVatW6DkyMjKUmJgoX1/fEqgQAACURXYNQBkZGYqPj1d8fLwkKSkpSfHx8UpOTta1a9f0xBNPaNeuXfrwww+VnZ2t1NRUpaam6urVq9Y5unTpotmzZ1vXx44dq82bN+vYsWPatm2bevbsqXLlyqlfv36lfXgAAMBB2fUW2K5du9SpUyfr+pgxYyRJERERmjx5sj7//HNJUvPmzW3GffPNN+rYsaMkKTExUWfOnLFu+/XXX9WvXz+dPXtWXl5eevDBB7Vjxw55eXmV7MEAAIAyw64BqGPHjjIMI9/tBW277tixYzbrS5cuvd2yAADAHa5It8A6d+6stLS0XO3p6enq3Lnz7dYEAABQoooUgOLi4myew7nuypUr2rJly20XBQAAUJIKdQvsp59+sv75wIEDSk1Nta5nZ2dr/fr1uueee4qvOgAAgBJQqADUvHlz6y+s53Wrq1KlSvr3v/9dbMUBAACUhEIFoKSkJBmGodq1a+uHH36webOqYsWKql69usqVK1fsRQIAABSnQgWgGjVqSJJycnJKpBgAAIDSUOTX4I8cOaJvvvlGp06dyhWIJk6ceNuFAQAAlJQiBaD58+frmWee0d133y0fHx9ZLBbrNovFQgACAAAOrUgBaNq0aZo+fbrGjx9f3PUAAACUuCJ9D9D58+fVu3fv4q4FAACgVBQpAPXu3VsbNmwo7loAAABKRZFugdWpU0cTJkzQjh071LRpU1WoUMFm+6hRo4qlOAAAgJJQpAD0n//8R1WqVNHmzZu1efNmm20Wi4UABAAAHFqRAlBSUlJx1wEAAFBqivQMEAAAQFlWpCtAgwcPLnD7ggULilQMAABAaShSADp//rzN+rVr17R//36lpaXl+SOpAAAAjqRIAWjlypW52nJycvTMM8/ovvvuu+2iAAAASlKRfwvsRk5OThozZow6duyocePGFde0AIpRqxcW27sEh7DSzd4VALC3Yn0IOjExUX/88UdxTgkAAFDsinQFaMyYMTbrhmEoJSVFa9euVURERLEUBgAAUFKKFIB+/PFHm3UnJyd5eXnpjTfeuOkbYgAAAPZWpAD0zTffFHcdAAAApea2HoI+ffq0EhISJEn169eXl5dXsRQFAABQkor0EHRmZqYGDx4sX19ftW/fXu3bt5efn5+GDBmiS5cuFXeNAAAAxapIAWjMmDHavHmzvvjiC6WlpSktLU2rV6/W5s2b9fzzzxd3jQAAAMWqSLfAVqxYoU8//VQdO3a0tj3yyCOqVKmS+vTpo7lz5xZXfQAAAMWuSFeALl26JG9v71zt1atX5xYYAABweEUKQMHBwZo0aZKuXLlibbt8+bKioqIUHBxcbMUBAACUhCLdAps1a5a6deume++9V4GBgZKkvXv3ytnZWRs2bCjWAgEAAIpbka4ANW3aVEeOHFF0dLSaN2+u5s2b65VXXtHRo0fVuHHjW57n22+/Vffu3eXn5yeLxaJVq1bZbDcMQxMnTpSvr68qVaqkkJAQHTly5KbzzpkzRzVr1pSLi4uCgoL0ww8/FPYQAQDAHaxIV4Cio6Pl7e2tYcOG2bQvWLBAp0+f1vjx429pnszMTAUGBmrw4MF6/PHHc22fMWOG3n77bS1atEi1atXShAkTFBoaqgMHDsjFxSXPOZctW6YxY8Zo3rx5CgoK0qxZsxQaGqqEhARVr1698AcLAADuOEW6AvTuu++qQYMGudobN26sefPm3fI8YWFhmjZtmnr27Jlrm2EYmjVrll5++WX16NFDzZo10+LFi/X777/nulL0V2+++aaGDRumQYMGqVGjRpo3b54qV66sBQsW3HJdAADgzlakAJSamipfX99c7V5eXkpJSbntoiQpKSlJqampCgkJsbZ5eHgoKChI27dvz3PM1atXtXv3bpsxTk5OCgkJyXcMAAAwnyIFIH9/f23dujVX+9atW+Xn53fbRUl/hixJuV639/b2tm670ZkzZ5SdnV2oMZKUlZWl9PR0mwUAANy5ivQM0LBhwzR69Ghdu3ZNnTt3liTFxsZq3LhxZfKboKOjoxUVFWXvMgAAQCkpUgB64YUXdPbsWf3jH//Q1atXJUkuLi4aP368IiMji6UwHx8fSdLJkydtbredPHlSzZs3z3PM3XffrXLlyunkyZM27SdPnrTOl5fIyEiNGTPGup6eni5/f//bqB4AADiyIt0Cs1gsevXVV3X69Gnt2LFDe/fu1blz5zRx4sRiK6xWrVry8fFRbGystS09PV3ff/99vl+2WLFiRbVq1cpmTE5OjmJjYwv8gkZnZ2e5u7vbLAAA4M5VpCtA11WpUkVt2rQp8viMjAwdPXrUup6UlKT4+HhVrVpVAQEBGj16tKZNm6a6detaX4P38/NTeHi4dUyXLl3Us2dPjRw5UtKfP9QaERGh1q1bq23btpo1a5YyMzM1aNCgItcJAADuLLcVgG7Xrl271KlTJ+v69dtQERERiomJ0bhx45SZmanhw4crLS1NDz74oNavX2/zHUCJiYk6c+aMdb1v3746ffq0Jk6cqNTUVDVv3lzr16/P87fLAACAOdk1AHXs2FGGYeS73WKxaMqUKZoyZUq+fY4dO5arbeTIkdYrQgAAADcq0jNAAAAAZRkBCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmI7DB6CaNWvKYrHkWkaMGJFn/5iYmFx9XVxcSrlqAADgyMrbu4Cb2blzp7Kzs63r+/fvV9euXdW7d+98x7i7uyshIcG6brFYSrRGAABQtjh8APLy8rJZf+WVV3TfffepQ4cO+Y6xWCzy8fEp6dIAAEAZ5fC3wP7q6tWrWrJkiQYPHlzgVZ2MjAzVqFFD/v7+6tGjh37++edSrBIAADi6MhWAVq1apbS0NA0cODDfPvXr19eCBQu0evVqLVmyRDk5OWrXrp1+/fXXfMdkZWUpPT3dZgEAAHeuMhWA3n//fYWFhcnPzy/fPsHBwRowYICaN2+uDh066LPPPpOXl5fefffdfMdER0fLw8PDuvj7+5dE+QAAwEGUmQB0/Phxbdq0SUOHDi3UuAoVKqhFixY6evRovn0iIyN14cIF63LixInbLRcAADiwMhOAFi5cqOrVq+vRRx8t1Ljs7Gzt27dPvr6++fZxdnaWu7u7zQIAAO5cZSIA5eTkaOHChYqIiFD58rYvrg0YMECRkZHW9SlTpmjDhg365ZdftGfPHv2///f/dPz48UJfOQIAAHcuh38NXpI2bdqk5ORkDR48ONe25ORkOTn9N8edP39ew4YNU2pqqu666y61atVK27ZtU6NGjUqzZAAA4MDKRAB6+OGHZRhGntvi4uJs1mfOnKmZM2eWQlUAAKCsKhO3wAAAAIoTAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJgOAQgAAJiOQwegyZMny2Kx2CwNGjQocMzy5cvVoEEDubi4qGnTplq3bl0pVQsAAMoKhw5AktS4cWOlpKRYl++++y7fvtu2bVO/fv00ZMgQ/fjjjwoPD1d4eLj2799fihUDAABH5/ABqHz58vLx8bEud999d75933rrLXXr1k0vvPCCGjZsqKlTp6ply5aaPXt2KVYMAAAcncMHoCNHjsjPz0+1a9dW//79lZycnG/f7du3KyQkxKYtNDRU27dvL+kyAQBAGVLe3gUUJCgoSDExMapfv75SUlIUFRWlhx56SPv375ebm1uu/qmpqfL29rZp8/b2VmpqaoH7ycrKUlZWlnU9PT29eA4AAAA4JIcOQGFhYdY/N2vWTEFBQapRo4Y++eQTDRkypNj2Ex0draioqGKbDwAAODaHvwX2V56enqpXr56OHj2a53YfHx+dPHnSpu3kyZPy8fEpcN7IyEhduHDBupw4caLYagYAAI6nTAWgjIwMJSYmytfXN8/twcHBio2NtWnbuHGjgoODC5zX2dlZ7u7uNgsAALhzOXQAGjt2rDZv3qxjx45p27Zt6tmzp8qVK6d+/fpJkgYMGKDIyEhr/+eee07r16/XG2+8oUOHDmny5MnatWuXRo4caa9DAAAADsihnwH69ddf1a9fP509e1ZeXl568MEHtWPHDnl5eUmSkpOT5eT03wzXrl07ffTRR3r55Zf14osvqm7dulq1apWaNGlir0MAAAAOyKED0NKlSwvcHhcXl6utd+/e6t27dwlVBAAA7gQOfQsMAACgJBCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6Th0AIqOjlabNm3k5uam6tWrKzw8XAkJCQWOiYmJkcVisVlcXFxKqWIAAFAWOHQA2rx5s0aMGKEdO3Zo48aNunbtmh5++GFlZmYWOM7d3V0pKSnW5fjx46VUMQAAKAvK27uAgqxfv95mPSYmRtWrV9fu3bvVvn37fMdZLBb5+PiUdHkAAKCMcugrQDe6cOGCJKlq1aoF9svIyFCNGjXk7++vHj166Oeffy6N8gAAQBlRZgJQTk6ORo8erQceeEBNmjTJt1/9+vW1YMECrV69WkuWLFFOTo7atWunX3/9Nd8xWVlZSk9Pt1kAAMCdy6Fvgf3ViBEjtH//fn333XcF9gsODlZwcLB1vV27dmrYsKHeffddTZ06Nc8x0dHRioqKKtZ6AQCA4yoTV4BGjhypNWvW6JtvvtG9995bqLEVKlRQixYtdPTo0Xz7REZG6sKFC9blxIkTt1syAABwYA59BcgwDD377LNauXKl4uLiVKtWrULPkZ2drX379umRRx7Jt4+zs7OcnZ1vp1QAAFCGOHQAGjFihD766COtXr1abm5uSk1NlSR5eHioUqVKkqQBAwbonnvuUXR0tCRpypQpuv/++1WnTh2lpaXptdde0/HjxzV06FC7HQcAAHAsDh2A5s6dK0nq2LGjTfvChQs1cOBASVJycrKcnP57J+/8+fMaNmyYUlNTddddd6lVq1batm2bGjVqVFplAwAAB+fQAcgwjJv2iYuLs1mfOXOmZs6cWUIVAQCAO0GZeAgaAACgOBGAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6ZSJADRnzhzVrFlTLi4uCgoK0g8//FBg/+XLl6tBgwZycXFR06ZNtW7dulKqFAAAlAUOH4CWLVumMWPGaNKkSdqzZ48CAwMVGhqqU6dO5dl/27Zt6tevn4YMGaIff/xR4eHhCg8P1/79+0u5cgAA4KgcPgC9+eabGjZsmAYNGqRGjRpp3rx5qly5shYsWJBn/7feekvdunXTCy+8oIYNG2rq1Klq2bKlZs+eXcqVAwAAR+XQAejq1avavXu3QkJCrG1OTk4KCQnR9u3b8xyzfft2m/6SFBoamm9/AABgPuXtXUBBzpw5o+zsbHl7e9u0e3t769ChQ3mOSU1NzbN/ampqvvvJyspSVlaWdf3ChQuSpPT09KKWLknKzrp8W+PvFBcrZNu7BIdwu+dTceCc/BPn5J84Jx0H5+SfbvecvD7eMIyb9nXoAFRaoqOjFRUVlavd39/fDtXceZrYuwBHEe1h7wrwfzgn/w/npMPgnPw/xXROXrx4UR4eBc/l0AHo7rvvVrly5XTy5Emb9pMnT8rHxyfPMT4+PoXqL0mRkZEaM2aMdT0nJ0fnzp1TtWrVZLFYbuMIkJ6eLn9/f504cULu7u72LgfgnITD4ZwsPoZh6OLFi/Lz87tpX4cOQBUrVlSrVq0UGxur8PBwSX+Gk9jYWI0cOTLPMcHBwYqNjdXo0aOtbRs3blRwcHC++3F2dpazs7NNm6en5+2Wj79wd3fnP2w4FM5JOBrOyeJxsys/1zl0AJKkMWPGKCIiQq1bt1bbtm01a9YsZWZmatCgQZKkAQMG6J577lF0dLQk6bnnnlOHDh30xhtv6NFHH9XSpUu1a9cu/ec//7HnYQAAAAfi8AGob9++On36tCZOnKjU1FQ1b95c69evtz7onJycLCen/77M1q5dO3300Ud6+eWX9eKLL6pu3bpatWqVmjThDisAAPiTxbiVR6WBIsrKylJ0dLQiIyNz3WYE7IFzEo6Gc9I+CEAAAMB0HPqLEAEAAEoCAQgAAJgOAQh217FjR5uvLSiuvkBBYmJibL7uYvLkyWrevPktjS1MX6Ck3Xgu49YQgEzs9OnTeuaZZxQQECBnZ2f5+PgoNDRUW7duLdU6PvvsM02dOrVU9wnHNnDgQFksllxLt27dSmyfY8eOVWxsbInNjztHfufn0aNH7V0aCsHhX4NHyenVq5euXr2qRYsWqXbt2jp58qRiY2N19uzZUq2jatWqpbo/lA3dunXTwoULbdpK8g2ZKlWqqEqVKiU2P+4seZ2fXl5eNutXr15VxYoVS7MsFAJXgEwqLS1NW7Zs0auvvqpOnTqpRo0aatu2rSIjI/XYY49JkiwWi+bOnauwsDBVqlRJtWvX1qeffmozz/jx41WvXj1VrlxZtWvX1oQJE3Tt2jXr9uu3Cj744APVrFlTHh4eevLJJ3Xx4kVrnxtva73zzjuqW7euXFxc5O3trSeeeMJmnzk5ORo3bpyqVq0qHx8fTZ48ufg/INjd9auSf13uuusuSX+em++995569uypypUrq27duvr8889txn/++efW86hTp05atGiRLBaL0tLS8tzfjbe14uLi1LZtW7m6usrT01MPPPCAjh8/bjOmoPMad7a8zs8uXbpo5MiRGj16tO6++26FhoZKkvbv36+wsDBVqVJF3t7e+tvf/qYzZ85Y5+rYsaNGjRpV4N9raWlpevrpp+Xt7S0XFxc1adJEa9assenz1VdfqWHDhqpSpYq6deumlJSUEv8cyjICkEld/7/dVatWKSsrK99+EyZMUK9evbR37171799fTz75pA4ePGjd7ubmppiYGB04cEBvvfWW5s+fr5kzZ9rMkZiYqFWrVmnNmjVas2aNNm/erFdeeSXP/e3atUujRo3SlClTlJCQoPXr16t9+/Y2fRYtWiRXV1d9//33mjFjhqZMmaKNGzfexqeBsigqKkp9+vTRTz/9pEceeUT9+/fXuXPnJElJSUl64oknFB4err179+rpp5/WSy+9dMtz//HHHwoPD1eHDh30008/afv27Ro+fLjNbwMW5ryGeSxatEgVK1bU1q1bNW/ePKWlpalz585q0aKFdu3apfXr1+vkyZPq06dPrnH5/b2Wk5OjsLAwbd26VUuWLNGBAwf0yiuvqFy5ctbxly5d0uuvv64PPvhA3377rZKTkzV27NhSPfYyx4Bpffrpp8Zdd91luLi4GO3atTMiIyONvXv3WrdLMv7+97/bjAkKCjKeeeaZfOd87bXXjFatWlnXJ02aZFSuXNlIT0+3tr3wwgtGUFCQdb1Dhw7Gc889ZxiGYaxYscJwd3e36f9XHTp0MB588EGbtjZt2hjjx4+/+QGjzIiIiDDKlStnuLq62izTp083DOPPc/Pll1+29s/IyDAkGV9++aVhGIYxfvx4o0mTJjZzvvTSS4Yk4/z584ZhGMbChQsNDw8P6/ZJkyYZgYGBhmEYxtmzZw1JRlxcXJ713cp5jTtXXufnE088YXTo0MFo0aKFTd+pU6caDz/8sE3biRMnDElGQkKCYRg3/3vtq6++MpycnKz9b7Rw4UJDknH06FFr25w5cwxvb+/bPtY7Gc8AmVivXr306KOPasuWLdqxY4e+/PJLzZgxQ++9954GDhwoSbl+RDY4OFjx8fHW9WXLluntt99WYmKiMjIy9Mcff+T6Mb+aNWvKzc3Nuu7r66tTp07lWVPXrl1Vo0YN1a5dW926dVO3bt2stzmua9asmc2YguZD2dWpUyfNnTvXpu2vz4v99TxwdXWVu7u79TxISEhQmzZtbMa2bdv2lvddtWpVDRw4UKGhoeratatCQkLUp08f+fr6WvsU5rzGnefG89PV1VX9+vVTq1atbPrt3btX33zzTZ7PlyUmJqpevXqSCv57LT4+Xvfee6+1b14qV66s++67L8/xyBu3wEzOxcVFXbt21YQJE7Rt2zYNHDhQkyZNuqWx27dvV//+/fXII49ozZo1+vHHH/XSSy/p6tWrNv0qVKhgs26xWJSTk5PnnG5ubtqzZ48+/vhj+fr6auLEiQoMDLR5bqMw86HscnV1VZ06dWyWvwagkj4PFi5cqO3bt6tdu3ZatmyZ6tWrpx07dpTa/uHYbjw/r4djV1dXm34ZGRnq3r274uPjbZYjR47Y3N4v6HyqVKnSTevJa7zBDz0UiAAEG40aNVJmZqZ1/a9/4V9fb9iwoSRp27ZtqlGjhl566SW1bt1adevWzfWQaFGUL19eISEhmjFjhn766ScdO3ZMX3/99W3PC/OoX7++du3aZdO2c+fOQs/TokULRUZGatu2bWrSpIk++uij4ioRJtGyZUv9/PPPqlmzZq5Af2NYyk+zZs3066+/6vDhwyVcrbkQgEzq7Nmz6ty5s5YsWaKffvpJSUlJWr58uWbMmKEePXpY+y1fvlwLFizQ4cOHNWnSJP3www8aOXKkJKlu3bpKTk7W0qVLlZiYqLffflsrV668rbrWrFmjt99+W/Hx8Tp+/LgWL16snJwc1a9f/7bmRdmTlZWl1NRUm+Wvb84U5Omnn9ahQ4c0fvx4HT58WJ988oliYmIkyeZB5vwkJSUpMjJS27dv1/Hjx7VhwwYdOXLEGv6BWzVixAidO3dO/fr1086dO5WYmKivvvpKgwYNUnZ29i3N0aFDB7Vv3169evXSxo0blZSUpC+//FLr168v4ervbAQgk6pSpYqCgoI0c+ZMtW/fXk2aNNGECRM0bNgwzZ4929ovKipKS5cuVbNmzbR48WJ9/PHHatSokSTpscce0//+7/9q5MiRat68ubZt26YJEybcVl2enp767LPP1LlzZzVs2FDz5s3Txx9/rMaNG9/WvCh71q9fL19fX5vlwQcfvKWxtWrV0qeffqrPPvtMzZo109y5c61vgd3KdwlVrlxZhw4dUq9evVSvXj0NHz5cI0aM0NNPP31bxwTz8fPz09atW5Wdna2HH35YTZs21ejRo+Xp6Sknp1v/J3jFihVq06aN+vXrp0aNGmncuHG3HKCQN34NHvmyWCxauXKlwsPD7V0KcNumT5+uefPm6cSJE/YuBYAD4C0wAHekd955R23atFG1atW0detWvfbaa9bbtwBAAAJwRzpy5IimTZumc+fOKSAgQM8//7wiIyPtXRYAB8EtMAAAYDo8BA0AAEyHAAQAAEyHAAQAAEyHAAQAAEyHAAQAAEyHAASYRMeOHTV69Gh7l2E6qamp6tq1q1xdXeXp6WnvcqwsFotWrVpl7zIAu+F7gACgBM2cOVMpKSmKj4+Xh4eHvcsB8H8IQABQBNeuXVOFChVu2i8xMVGtWrVS3bp1i7yvq1evqmLFikWuAUBu3AIDTOiDDz5Q69at5ebmJh8fHz311FM6deqUdXtcXJwsFotiY2PVunVrVa5cWe3atVNCQoLNPNOmTVP16tXl5uamoUOH6p///KeaN29u3Z7Xbbfw8HANHDjwlmuRpM8//1x169aVi4uLOnXqpEWLFslisSgtLc3a57vvvtNDDz2kSpUqyd/fX6NGjVJmZuYtfR41a9bU1KlT1a9fP7m6uuqee+7RnDlzbPpYLBbNnTtXjz32mFxdXTV9+nRJ0urVq9WyZUu5uLiodu3aioqK0h9//GGdd8WKFVq8eLEsFov1uNPS0jR06FB5eXnJ3d1dnTt31t69e637mjx5spo3b6733ntPtWrVkouLS5FrkP78Vuz27dvLxcVFjRo10saNG2/pcwHuaAYAU+jQoYPx3HPPGYZhGO+//76xbt06IzEx0di+fbsRHBxshIWFWft+8803hiQjKCjIiIuLM37++WfjoYceMtq1a2fts2TJEsPFxcVYsGCBkZCQYERFRRnu7u5GYGBgnvu8rkePHkZERIR1/Wa1/PLLL0aFChWMsWPHGocOHTI+/vhj45577jEkGefPnzcMwzCOHj1quLq6GjNnzjQOHz5sbN261WjRooUxcODAW/psatSoYbi5uRnR0dFGQkKC8fbbbxvlypUzNmzYYO0jyahevbqxYMECIzEx0Th+/Ljx7bffGu7u7kZMTIyRmJhobNiwwahZs6YxefJkwzAM49SpU0a3bt2MPn36GCkpKUZaWpphGIYREhJidO/e3di5c6dx+PBh4/nnnzeqVatmnD171jAMw5g0aZLh6upqdOvWzdizZ4+xd+/eIteQnZ1tNGnSxOjSpYsRHx9vbN682WjRooUhyVi5cuUtfT7AnYgABJhEXmHkup07dxqSjIsXLxqG8d8AtGnTJmuftWvXGpKMy5cvG4ZhGEFBQcaIESNs5nnggQcKHYBuVsv48eONJk2a2PR56aWXbALQkCFDjOHDh9v02bJli+Hk5GSttyA1atQwunXrZtPWt29fmyAmyRg9erRNny5duhj/+te/bNo++OADw9fX17p+4/Fu2bLFcHd3N65cuWIz7r777jPeffddwzD+DEAVKlQwTp06ZdOnKDV89dVXRvny5Y3ffvvNuv3LL78kAMH0uAUGmNDu3bvVvXt3BQQEyM3NTR06dJAkJScn2/Rr1qyZ9c++vr6SZL09lZCQoLZt29r0v3G9OGpJSEhQmzZtCtzP3r17FRMToypVqliX0NBQ5eTkKCkp6ZbqCA4OzrV+8OBBm7bWrVvn2u+UKVNs9jts2DClpKTo0qVLee5n7969ysjIULVq1WzGJSUlKTEx0dqvRo0a8vLyyjW+sDUcPHhQ/v7+8vPzy/dYATPiIWjAZDIzMxUaGqrQ0FB9+OGH8vLyUnJyskJDQ3X16lWbvn99wNZisUiScnJybnlfTk5OMm74veVr164VqZaCZGRk6Omnn9aoUaNybQsICLjleW7G1dU1136joqL0+OOP5+p7/bmdG2VkZMjX11dxcXG5tv31Nfkb91WcNQAgAAGmc+jQIZ09e1avvPKK/P39JUm7du0q9Dz169fXzp07NWDAAGvbzp07bfp4eXkpJSXFup6dna39+/erU6dOt1xL/fr1tW7dOpu2G/fTsmVLHThwQHXq1Cn0cVy3Y8eOXOsNGzYscEzLli2VkJBQqP22bNlSqampKl++vGrWrFmUUgtVQ8OGDXXixAmlpKRYr+LdeKyAGXELDDCZgIAAVaxYUf/+97/1yy+/6PPPP9fUqVMLPc+zzz6r999/X4sWLdKRI0c0bdo0/fTTT9YrRZLUuXNnrV27VmvXrtWhQ4f0zDPP2Ly5dSu1PP300zp06JDGjx+vw4cP65NPPlFMTIyk/16VGj9+vLZt26aRI0cqPj5eR44c0erVqzVy5MhbPp6tW7dqxowZOnz4sObMmaPly5frueeeK3DMxIkTtXjxYkVFRennn3/WwYMHtXTpUr388sv5jgkJCVFwcLDCw8O1YcMGHTt2TNu2bdNLL71UpCB6sxpCQkJUr149RUREaO/evdqyZYteeumlQu8HuNMQgACT8fLyUkxMjJYvX65GjRrplVde0euvv17oefr376/IyEiNHTtWLVu2VFJSkgYOHGhz22Xw4MGKiIjQgAED1KFDB9WuXdt69edWa6lVq5Y+/fRTffbZZ2rWrJnmzp1r/Qfc2dlZ0p/PKm3evFmHDx/WQw89pBYtWmjixIk2z73czPPPP69du3apRYsWmjZtmt58802FhoYWOCY0NFRr1qzRhg0b1KZNG91///2aOXOmatSoke8Yi8WidevWqX379ho0aJDq1aunJ598UsePH5e3t/ct13urNTg5OWnlypW6fPmy2rZtq6FDh1pfnwfMzGLceIMeAIqoa9eu8vHx0QcffFCi+5k+fbrmzZunEydOFMt8NWvW1OjRo/mpEMBEeAYIQJFcunRJ8+bNU2hoqMqVK6ePP/5YmzZtKpEv2XvnnXfUpk0bVatWTVu3btVrr71WqNtbAHAjAhCAIrl+K2f69Om6cuWK6tevrxUrVigkJKTY93X9GaNz584pICBAzz//vCIjI29p7JYtWxQWFpbv9oyMjOIqE0AZwi0wAHe0y5cv67fffst3++28OQag7CIAAQAA0+EtMAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDoEIAAAYDr/H/uclYq3x8bLAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* French users are the only ones that have a higher amount of not converted users than subscribers.\n",
        "* English has a significantly higher amount of converted users compared to Spanish users."
      ],
      "metadata": {
        "id": "WmKGhyeiiI5y"
      },
      "id": "WmKGhyeiiI5y"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Language preferred, Converted, and Landing page"
      ],
      "metadata": {
        "id": "kGDeNwCl7o4H"
      },
      "id": "kGDeNwCl7o4H"
    },
    {
      "cell_type": "code",
      "source": [
        "# Comparing the above graph including landing page\n",
        "sns.catplot(data=data, hue='converted', x='language_preferred',col='landing_page',kind='count');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "djgInfAx7u93",
        "outputId": "678cba16-f7b3-4b64-f50d-376dfce8072a"
      },
      "id": "djgInfAx7u93",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1081.36x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* English users are more likely to convert on either page.\n",
        "* All users are likely to convert on the new landing page."
      ],
      "metadata": {
        "id": "Yy2xi8na8VEZ"
      },
      "id": "Yy2xi8na8VEZ"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "27906576"
      },
      "source": [
        "## 1. Do the users spend more time on the new landing page than the existing landing page?"
      ],
      "id": "27906576"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5fdf0c8"
      },
      "source": [
        "### Visual Analysis"
      ],
      "id": "e5fdf0c8"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6eZJa41eg21n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        },
        "outputId": "47f87392-b2b2-4011-d701-14b9deadbe2c"
      },
      "source": [
        "# Visual between time spent and landing page\n",
        "sns.boxplot(data=data,x='landing_page',y='time_spent_on_the_page',showmeans=True)\n",
        "plt.show()\n",
        "g = sns.FacetGrid(data, col=\"landing_page\")\n",
        "g.map(sns.histplot, \"time_spent_on_the_page\",kde=True, bins=10);"
      ],
      "id": "6eZJa41eg21n",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x300 with 2 Axes>"
            ],
            "image/png": 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Gpobz0+LFbC7UQ0QiX2ccYVinSPokhaLTychTW6Bp0TR8+HDUH8c4f0en0/H000/z9NNPt2IqIYQQot6OI2X8mJkPQMfoQEZ1jcFoOPm60GFUcmT2dM55eC65dQEs22PBarMztGOEFE5tgJx7TgghhDiB7CIri3fWF0y9E0O5tHvsKQsmJ3sdnXxKObdTJAAbsotZn1XcklFFK5GiSQghhPiDYmsN32zNxaGgc0wg53WOPKORIp0O+iaHcW7H+sJp5b5CdhyRBVM9nRRNQgghxO/YHYpF2/KoqXMQH+LHyK4xzd611rddGP3bhQHw864CjpbbTvMI4c6kaBJCCCF+Z+3BIgrKbZiMei7uHotRf3YflUM6RJASYcbuUHyzNRfbsSPwhOeRokkIIYQ4Jr+smnUHiwD4U5dogvx8zrpNnU7HqG6xBPkZKa2qZcnuo2fdptCGFE1CCCEEoJRiya6jqGPzmDrHBLmsbT8fA5d0j0UH7Mwr54Cl0mVti9YjRZMQQggBZOaVk1dWjY9Bx3mdolzeflyIP72TQwH4eWcBtjrZTedppGgSQgjh9WodsGKvBYBBqREEmFpmGcPB7SMI8fehwlbH6n1FLbIN0XKkaBJCCOH19pQZsNbYCfX3oXdSaIttx8eg509dogHIOFxCUWVNi21LuJ4UTUIIIbya3i+QPeX1H4dDOkRg0Lfsyt3J4WbaRwagFPy6RyaFexIpmoQQQni14AF/oU7piAz0pWN0YKtsc1inSPQ6yCq0clAmhXsMKZqEEEJ4rTKbg6D+fwbgnPatd364MLOvczfgyv2FpzwPq3Afmp6wVwghhGiO7OxsLBbLWbfz/qos9L7+hPo4aB8Z4IJkTde/XThbD5dytNzGvqOVrTbKJZpPiiYhhBAeJTs7my7p6VRZrWfVjs5oIuHudzGY/Wjna221UaYG/r4G+iSFsfZgEav3F9IhKqDVM4gzI0WTEEIIj2KxWKiyWhk77QVikjs0u5195Xo2FxupK8kjxN/hwoRN1zc5lIxDJRRW1rCnoMKlC2oK15OiSQghhEeKSe5AYqduzXqsQyl+WpUF1FK2fgG6P1/h2nBNZPIx0CcplNUHilh3sIhO0YEy2uTGZCK4EEIIr3PQUklpVS1G7FRs+VHTLL2SQvEx6LBU1JBddHa7HEXLkqJJCCGE19lyqBSAGEpQtdWaZvHzMdAtPgSA9VnFmmYRpyZFkxBCCK9SYq0h69iITizuUaT0SQ5Fr4NDxVXklWlbxImTk6JJCCGEV9l6uH6UqV2EGX9qNU5TL9jPxzkJfHNOibZhxElJ0SSEEMJr1Nkd7DhSBkDPxBCN0zTWsNjl3vwKrDV12oYRJyRFkxBCCK+x92gF1XUOgvyMpES07mKWpxMT7EdMsAm7Umw7VtgJ9yJFkxBCCK/RMMrUNS4YvRse2t8rMRSArYdKcTjk1CruRoomIYQQXqGsqpac4iqgvmhyR52iA/H3MVBhq+NAoZzI191I0SSEEMIrZObVjzIlhvkT7O+jcZoTMxr0dIuvL+gyZEK425GiSQghRJunlCIztxxw31GmBj0SQtABOcVVlLnHwX3iGCmahBBCtHlHSqoprarF16CnY3Sg1nFOKdjfh9TI+knqBysMGqcRvydFkxBCiDZve2792kydYgLxMbj/R1/DLrrsSj3opXByF279zrHb7Tz22GOkpqbi7+9Phw4deOaZZ1BKjigQQgjRNDV1DvYWVADuv2uuQUpEAGZfAzaHDv/2/bWOI44xah3gVGbNmsUbb7zBe++9R7du3Vi/fj233HILISEhTJo0Set4QgghPMD+oxXU2hWh/j7EhfhpHadJ9Hod6bHBbMguJrDHCK3jiGPcumhauXIlo0eP5rLLLgMgJSWFjz/+mLVr12qcTAghhKfYlV8/ATwtNgidG67NdDJd4+uLJv8OAyiptmsdR+Dmu+eGDBnC4sWL2b17NwAZGRksX76cSy65RONkQgghPEFVjZ3sYyfnTTt2bjdPER7gS7ivA53ByNKsKq3jCNx8pOnhhx+mrKyMLl26YDAYsNvtPPvss4wdO/akj7HZbNhsNuflsrK2vRR9dnY2FovF5e1GRkaSnJzs8naFcFfe1nd4iz0F5TgURAWZCAvw1TrOGWsX4KCoRs/PB6p4UimPGilri9y6aPr000+ZO3cuH330Ed26dWPz5s1MmTKF+Ph4xo8ff8LHzJgxg6eeeqqVk2ojOzubLunpVFmtLm/b32xmZ2amFE7Ca3hT3+FNdufXTwD3tFGmBkkBDjYU2Mgpgy2HSul17KS+QhtuXTQ9+OCDPPzww1x//fUA9OjRg6ysLGbMmHHSomn69OlMnTrVebmsrIykpKRWydvaLBYLVVYrY6e9QExyB5e1m5+9j7mzHsRisUjRJLyGN/Ud3qK8upbDJfW7tTrHuPfaTCfjo4eq3asI6DacLzcdlqJJY25dNFmtVvT6xtOuDAYDDofjpI8xmUyYTKaWjuZWYpI7kNipm9YxhPBo3th3tHUNo0zxoX4E+bnnaVOaomLHEgK6DWfhllz+flk6Rg9YZ6qtcuui6YorruDZZ58lOTmZbt26sWnTJl566SVuvfVWraMJIYRwc7sbjprz0F1zDaoPbiLYpMdSYWPlvkLO6xyldSSv5dbl6quvvsrVV1/NPffcQ3p6Og888AB33nknzzzzjNbRhBBCuLFiaw0F5Tb0OugU7dlFEw47QxLr15dasPmIxmG8m1sXTUFBQbz88stkZWVRVVXFvn37+Mc//oGvr+cdASGEEKL1NKwAnhhmxt/X809Dcm47fwC+355Hda2s2aQVty6ahBBCiOZoKJo6ufnJeZsqLcKHhFB/Kmx1LM4s0DqO15KiSQghRJtSVlVLQbkNHdA+KkDrOC6h1+n4c+94ABZsPqxxGu8lRZMQQog2Ze/R+lGmhFB/zL5ufbzTGRl9rGhasusopdZajdN4JymahBBCtCkNu+Y6tpFdcw26xAbTJTaIGruDRdtztY7jlaRoEkII0WZU2OrILa0GoENU2yqaAC7rEQfAN1vzNE7inaRoEkII0WbsOzbKFBfiR6Bf29k11+DSnvVF08q9FkqsNRqn8T5SNAkhhGgzGuYzdWyDo0xQP3rWJTaIOofih+35WsfxOlI0CSGEaBOsNXUcLq4/11yHNjaf6fcude6ik3lNrU2KJiGEEG3C/qOVKCA6yESIv+eea+50GoqmFbKLrtVJ0SSEEKJNaDhqri2PMkH9UYFpMcd20e2QXXStSYomIYQQHq+61k5OsRWATm10PtPvNYw2fSu76FqVFE1CCCE83gFLJQ4FEQG+hAW0/fOTXtYzFqjfRScLXbaetnc8pnBr2dnZWCwWl7drs9kwmUwubzcyMpLk5GSXtyuEcK22vmsuMzPzuOuSgo3klNXxzndr+FOquUntSJ92dqRoEq0mOzubLunpVFmtLdC6DlAub9XfbGZnZqZ0MkK4sZo6B1lF9f1KW1tqoKzoKADjxo077raQoTcQOmwsz8/7iQc/e6pJ7UmfdnakaBKtxmKxUGW1MnbaC8Qkd3BZu5lrl/Lde69w2Z2Pktazn8vazc/ex9xZD2KxWKSDEcKNHSysxO5QhPr7EBnYtnbNVVWUAZywfyurhR9zIaBDf6579Qt8TzPhRvq0sydFk2h1MckdSOzUzWXt5WfvAyAivp1L2xVCeIbfn2tOp9NpnKZlnKx/21CWRVFlDdXBSbSPC9YgmXeRieBCCCE8Vp3dwcHCSqDtnaC3KTode8578ss1TuIdpGgSLUIpRYWtjsIKG/ll1ZRYa6ixu37OkRDCu2UVWam1K4L8jEQHuf5gEHfXUDRlF1mx1do1TtP2ye45cdbyy6pZta+QbYdL2ZlXTnaRlbyyamrqHMfdN3HSRyzONRJRdYTwAF/iQvyJDfbD39egQXIhhKdz7pqLaru75k4lItBEuNmXImsN+y2VpMsuuhYlRZM4Y0opMnPLWbjlCN9vz2Pf0cpT3t+g12F31I8yGfyDKamFkqOVxx5XDECY2YfUyADaRwUSF+KH3gs7PyHEmbGr+lOngHfummvQMSaQtQeK2FtQIUVTC5OiSTSZtaaOBZuP8MGqLHbkljmv1+uge0IIfZPDSI8Lon1UILHBfkQGmjAZ9ej1OursDlau28iIK67mqmkv4xseT0F5NXml1RRba+t/skvYmF2Cv4+BzjGBdIsPIcoLh9uFEE1TUK2jxu4gwNdAXIif1nE00ym6vmjKKrJiq7NjMsrIfUuRokmcVnl1Le+vyuK/y/ZTfGzlWV+jnj+lRXNpzzjO7xRFiPnUJ8c0GvQE+uqptWQRb1YkJoU6b6uutZNTZGWfpZKDlkqqau1kHCol41Ap0UEmeiSG0CU2CKNepuAJIX5zxFrfJ3Tw0l1zDSICfAkz+1BsreWApZIusTLa1FKkaBInZXcoPlidxUs/7HIWS8nhZm4e3I6r+yUSanbNeih+PgY6xQTRKSYIu0ORU2Rle24Z+49WUFBuY3FmAav2FdI7KZSeCSGYfORblBBeT6fnSFV90eTNu+YAdDodHaMDWXewmL0FFVI0tSApmsQJmZK688CPFrJK8wBoHxXApD914vKecRgNLTfiY9DrSIkMICUyAGtNHZm55WzOKaHCVsfKfYWsP1hM3+RQ+iSH4WuUkSchvJVfcg9qHDr8fPQkhPprHUdznaKDWHewmIOFVmrqHNI/thApmkQjNXUONhQaiL1xJlmldQT7GZk6sjPjzmnXosXSiZh9jfRrF0bvpFB255ezIauYwsoaVh8oIuNQKQNSwuiRGNKqmYQQ7sHceQhQv2tOr/feXXMNIgN9CfH3obSqloOFlXSOCdI6UpskRZNwOlxcxQ878iirNqCUg4s7BjLjxqGEa3zGcINeR3pcMF1ig9hTUMGqfYWUVNXy6x4LWw6VkkCApvmEEK3LoRT+nQcDbe9cc83VsItuQ1b9LjopmlqGjN8JHEqxcp+FzzYeoqy6DrNBkf/xo9zZL0Tzgun3dDodnWOCGHdOOy7sEo3Z10BJVS3bSSbqL49S5ZC5TkJ4g12WWoyB4fjoFEnhZq3juI2GhS4PWCqptR+/Tp44e1I0ebmqGjvzNx9m3cH69ZK6xgUzIq4WW85WjZOdnEGvo3tCCDcPbkef5FBAYe48mPXV0aw9UORcE0oI0TatOlQNQJy/A4PsmnOKDjIR7GekzqGcp5YRriVFkxfLK6vmo7XZ5BRVYdTruLhbLCO7xuDjIe8Kk9HAeZ2i6MN+qrMycKBj1f5CPlmfg6XCpnU8IUQLUEqx+nB90ZRgltGU32vYRQe/rZQuXMvtPx4PHz7MuHHjiIiIwN/fnx49erB+/XqtY3m8PfnlfLbhEBW2OkL9fbhuQBJpsZ65DzyAGvLnPUoX32L8jHqOltv4eG02aw8U4ZBRJyHalC2HSrFY7Thqqojxk//vP+oUXd+PH7BUUie76FyuWUVT+/btKSwsPO76kpIS2rdvf9ahGhQXFzN06FB8fHz47rvv2LFjBy+++CJhYWEu24a3UUqxIauYb7flYXcoUiMDuH5gEpGBnr/ydoyxinHntKN9ZAAOhXPUqbiyRutoQggX+XZbLgBV+9bRygf0eoSYYBOBJiO1dkVWkVXrOG1Os46eO3jwIHb78WdTttlsHD58+KxDNZg1axZJSUnMnj3beV1qaqrL2vc2Dodiye6jbD1cCkCvxBDO6xzVps7zFmAycnnPOHbll7Nk11EKym18vC6bC9Ki5ZxMQng4pRTfba1fO866awWMOkfjRO6nYRfd5pwS9hZU0EGOLnSpMyqavvrqK+fv33//PSEhv62RY7fbWbx4MSkpKS4L99VXXzFq1CiuueYali5dSkJCAvfccw+33377SR9js9mw2X6bz1JWVnbS+55IdnY2Foul2ZlPJjIykuTkZJe321R2h2LRtjz2Hq3fz31ep0j6JLfNETudTkeX2GASw8x8vz2PQ8VV/LAjn5xiK8M7R8uib+KEzrbvEC1vR24Z2UVWfA1QtX+D1nHcVqdjRdP+o5XUORxyCioXOqOiacyYMUD9h9L48eMb3ebj40NKSgovvviiy8Lt37+fN954g6lTp/LII4+wbt06Jk2ahK+v73HbbzBjxgyeeuqpZm0vOzubLunpVFldP6TpbzazMzNTk8Kpzu5g4dZcsgqtGHQ6RnWPce73bssCTUb+0ieBdQeLWLO/iMzccvJKq7mke5ycCFgc52z6DtE6GkaZ+sb6sae2WuM07isuxI8Ak4FKm53sIivtI2W0yVXOqGhyOOonlaWmprJu3ToiIyNbJNTvt9e/f3+ee+45APr06cO2bdt48803T1o0TZ8+nalTpzovl5WVkZSU1KTtWSwWqqxWxk57gZjkDmf/BI7Jz97H3FkPYrFYWr1oqqlz8HXGEQ6V1B8hd3nPONpFeM9ikHqdjkGpESSGmlm0PY9iay2frs9hZNcYWfxNNHI2fYdoeUop53ymwUl+fKJxHnem0+noGBVIxqFS9hZUSNHkQs2a03TgwAFX5zihuLg4unbt2ui69PR0Pv/885M+xmQyYTKd3ShCTHIHEjt1O6s23EF1rZ0Fm4+QV1aNr0HPn3vFkxDmnedoSgjz58aBySzankd2kZXvtuVxtNzG4A4RbWpOl2g+V/QdouXsKahg/9FKfA16+sXJ3+l0OkUHkXGolP1HK7E7lKxn5SLNPo3K4sWLWbx4MQUFBc4RqAbvvvvuWQcDGDp0KLt27Wp03e7du2nXrp1L2m/LbLV2vtx0mIJyG35GPWP6JBAT7Kd1LE35+xoY3TuelXsL2ZBdzPqsYiwVNi7uHovJKKuJC+HOvt1aP8p0XudIzJ6ymJyG4kL9MPsasNbYySm2kuJFexhaUrPeeU899RQXXXQRixcvxmKxUFxc3OjHVe6//35Wr17Nc889x969e/noo494++23mThxosu20RbZ6uzM33yEgnIb/j4GruqX6PUFUwO9TsewTpGM6haDQa/jYKGVeetyKLHKsgRCuLOG+UyXdI/TOIln0Ot0ziPnZKFL12nWSNObb77JnDlzuOmmm1ydp5EBAwbw5ZdfMn36dJ5++mlSU1N5+eWXGTt2bItu15PV2h18dWyXnJ9Rz1/6JLSJNZhcrUtsMOFmX77ekkuJtZZP1x/iil5xxIV45+5LIdzZvqMV7Movx8egY0R6DPt2FmgdySN0ig5k6+FS9hVUcEFatNZx2oRmFU01NTUMGTLE1VlO6PLLL+fyyy9vlW15ujq7g68yjnCktBrfYwWTHCV2ctHBflw/IImvMupH5T7feJiLu8U6T0MghHAPi7bVjzIN6RBJiNlH4zSeIyHUH38fA1W1dg6XVLn/KUA8QLNew7/+9a989NFHrs4izkKdw8HCLbkcKq7Cx6DjL70TiJZdcqcVYDJyVd9EUiMDsDsU32zNZVO263YxCyHOXsN8pkt7xGqcxLPo9To6RNXPZdqTX65xmrahWSNN1dXVvP322/z000/07NkTH5/Glf9LL73kknCiaRwKvt2aR1aRFaNex+heCcSGSMHUVL5GPZf3iHOulv7rHgvl1XXI2vNCaC+70Mr2I2UY9DpGdpWi6Ux1jA5k25Ey9h2tpJO8fGetWUXTli1b6N27NwDbtm1rdJtODt9uZTrWFxrIsVZi0Ou8elmBs6HX67ggLYoQfx+W77WwKaeEogAD6GRAWwgtfXdsbaZz2ocTHuCrcRrPkxhmxs+op6rWjsUmn89nq1lF0y+//OLqHKIZlFKE/ek2cqwG9Dq4rEccSeFmrWN5LJ1OR792Yfj7GPgpM5+sSgORo6dRa5czqQuhlW+3yVFzZ8Og1zlHm3Iq5Uvg2ZJX0IN9ubOS4AFjABiZHkNqpKzD4Qpd44O5rGccehQBaUN5dnkRlbY6rWMJ4XUOl1SRkVOCTgejusm+peZKi60/+8Fhqx4MzV6eUdDMkaYLLrjglLvhfv7552YHEk0zb202H26tn9jXM7SOLnHBGidqWzpEBTI0uo6lh+rYkg/j/m8Nc24ZSIi/HLkjRGtpOGpuYEq4HAl8FhJC/Qk0Gamw1eHfvr/WcTxas0aaevfuTa9evZw/Xbt2paamho0bN9KjRw9XZxR/8P32PB75cisApav+R6dgx2keIZoj2k+RP+9RAn11bMou4ab/W0OptVbrWEJ4je+OHTV3SXcZZTobOp2OzjH1S6kEdD1f4zSerVkjTf/6179OeP2TTz5JRYWsPNqS1uwv5L6PN+FQcGGqP+/Oeg+uG611rDarJnc3Tw2P4NkVZWw5VMrY/1vNh7cNItQsE1KFaEl5pdVsOLb8x8Uyn+mspcUGsTG7BP8OA7HWyhft5nLpnKZx48a57Lxz4ng7jpTx1/fWU1PnYGTXGO7qF6J1JK+QGurDx7efQ0SAL9sOl3HjO2soqpTTrgjRkr7ZmotSMCAlTJZQcYGoQBNBRoXex8Saw9Vax/FYLi2aVq1ahZ+fvLlbQnahlZvfXUu5rY6BKeG8ekMfOWt1K0qLDWLeHecQGWhiR24ZN76zmsIKm9axhGizFm45AsDlPeM1TtI26HQ6kgLsACzLlqKpuZq1e+7KK69sdFkpRW5uLuvXr+exxx5zSTDxG0uFjZveXYOlwkaX2CDeGd8fPx+D1rG8TqeY+sLpxndWszOvnBveWc3cv54jE1SFcLGcIiubskvQ6+ASWQXcZZLMDnaUwpZ8G0fLbdJ3NUOzRppCQkIa/YSHhzN8+HC+/fZbnnjiCVdn9GrWmjpum7OOrEIrSeH+vH+rHMGlpY7Rgcy74xxigk3szq9g3H9lV50QrvbN1oYFLSOIDpK9F64S6AO2I7twKPjm2EieODPNGmmaPXu2q3OIE6izO7jvo01kHColzOzDe7cMbNXzyWVmZrp1e63lRLkfGxrM40sK2ZVfzlWv/sKT50cQZGr6d5DIyEiSk5NdGROA7OxsLBaLy9ttqbxCnIjsmms5lTuWYopPY0HGESYMlZNFnamzWuVqw4YNzg+Ubt260adPH5eEEvW7PB9bsJ3FOwswGfX8d3x/2kcFtsq2y4qOAvUT+1uCpxxhebrXwRieSOwNMzhAGNe/uYz8eX9H1Vib1La/2czOzEyXFiLZ2dl0SU+nytq0DGeiJfIKcSIHLZVsO1x/rrmLZakBl7PuXEbkyDvYlF1CVmEl7SJkUeQz0ayiqaCggOuvv54lS5YQGhoKQElJCRdccAHz5s0jKirKlRm90n+W7OPjtdnodPDK9X3o1y681bZdVVEGwGV3Pkpaz34uazdz7VK+e+8Vqqs9YxJiU16H0hodvxYoiOtM72nzGBZdh89pBpzys/cxd9aDWCwWlxYhFouFKquVsdNeICa5g8vabam8QpxIwyjT0I6Rcq65FmCvLKZntC+b82v4fMMhpl6UpnUkj9Ksoum+++6jvLyc7du3k56eDsCOHTsYP348kyZN4uOPP3ZpSG/z+YZDvPD9LgCe+nM3zb5tRcS3I7FTN5e1l5+9z2VttaZTvQ6JQHSyjc83HqKoBjZUhDC6dzw+Bu3OUBST3MGlfzchWtPXGfXzmS7vKWsztZQLUs31RdPGw0wZ0Rm9HIndZM3q2RctWsR//vMfZ8EE0LVrV15//XW+++47l4XzRsv2HGXa51sAuPP89tw8OEXbQOK0ooJM/KVPAr4GPYdLqvg64wh1dlk8TogztSe/nF355fgYdHKuuRY0MN6PID8jh0uqWLW/UOs4HqVZRZPD4cDH5/gjuHx8fHA45MOiubYfKeXuDzdS51D8uVc800Z10TqSaKKYYD/G9InHx6Ajp7iKhVtzqZP/BSHOyNdb6keZzu8cJUcJtyCTUccVveon2X+24ZDGaTxLs4qmP/3pT0yePJkjR347ZPHw4cPcf//9XHjhhS4L500Ol1Rxy+x1VNjqGNw+gheu6SlDph4mLsSf0b0SMOp1ZBVa+XZrHnaH0jqWEB5BKSVHzbWia/olAvDdtlzKquWcmk3VrKLptddeo6ysjJSUFDp06ECHDh1ITU2lrKyMV1991dUZ27xSay3j311LQbmNtJgg3rypHyajLF7piRLC/LmiVzwGvY4DlkoWbcvDIYWTEKe1I7eM/UcrMRn1jOgao3WcNq93UigdowOprnXw7bERPnF6zZoInpSUxMaNG/npp5/YuXMnAOnp6YwYMcKl4bxBda2d2z9Yz96CCmKD/Zh9ywAZlvZwyeFmLu8Zx8KMXPYereD7HXmM6haLXicjh0KczMJjH9wXpEUTaDqr1XBEE+h0Oq7ul8jM73byvw2HuH6gHBnbFGc00vTzzz/TtWtXysrK0Ol0jBw5kvvuu4/77ruPAQMG0K1bN5YtW9ZSWdsch0Pxt/9lsPZAEUEmI3NuHUB8qL/WsYQLpEQEcGmPWPQ62J1fwY878nEoGXES4kR+v2uuYa6NaHlX9klAr4MNWcXsLSjXOo5HOKOi6eWXX+b2228nODj4uNtCQkK48847eemll1wWrq2b8V0m32zJxceg462b+tEl9vjXVXiu9lGBXNw9Fp0OduaV81NmPkoKJyGOszG7hJyiKgJ8DfypS7TWcbxGdLCf8/X+aE2Oxmk8wxkVTRkZGVx88cUnvf2iiy5iw4YNZx3KG7y7/ADvLDsAwD+v6cWQjpEaJxItoVN0EBd3qy+cMnPLWbyzAKmbhGhswebDAIzqFou/r8znbE1jB7UD4LMNOVTX2jVO4/7OqGjKz88/4VIDDYxGI0ePHj3rUG3dom15PPPNDgCmXdyF0b0TNE4kWlLnmCBGdY1FB2w/UsamYgMg85uEAKi1O5zzmUb3kb6wtZ3XOYqEUH/KquucfwdxcmdUNCUkJLBt27aT3r5lyxbi4mQV11PZnFPClE82oRSMOyeZu85vr3Uk0QrSYoO4qFsMOuBAhYHwkXfJrjohqF/Qt6iyhshAX4Z2iNA6jtcx6HXcOKh+EvjcNVkap3F/Z1Q0XXrppTz22GMnPHdYVVUVTzzxBJdffrnLwrU1BZV1/PW9dVTXOrggLYonr+iGTo6o8hpdYoMZ2TUGUAT1vYz/biqTwkl4vfmbfpsAbtTw9EPe7Jr+iRj1OjZll7D9SKnWcdzaGb1D//73v1NUVETnzp15/vnnWbBgAQsWLGDWrFmkpaVRVFTEo48+2lJZPZrOFMCzy4qxVNSQHhfMqzf2lQ7CC6XHBdMv3I5SDr7ba+XphTukcBJeq8JWxw878gAYI9MUNBMd5Oc8bc1Ha7I1TuPezuhTOyYmhpUrV9K9e3emT5/OX/7yF/7yl7/wyCOP0L17d5YvX05MTMstSjZz5kx0Oh1TpkxpsW20BIeCqDHTySmrIybYxLsT+ss6JF4sJdBB0aL6RWBnrzjIs99kSuEkvNIP2/OornWQGhlAz8QQreN4tbHHdtHN33SYclkh/KTOeKijXbt2fPvtt1gsFtasWcPq1auxWCx8++23pKamtkRGANatW8dbb71Fz549W2wbLUEpxaYiA/4pvfEz6nh3wgDiQmQtJm9XseVH7upX/yHx3+UHmPndTimchNeZv7l+19yY3gkyVUFjgztE0D4qgMoaO/9bL+ejO5lm7x8KCwtjwIABDBw4kLCwMFdmOk5FRQVjx47lnXfeafFtudr6rGIOVhpQDjt/GxxKt3j5NiXqXdTBzDNjugPw1q/7ee5bGXES3uNouY3le+qPth7dWxa01JpOp+OWofUDH3NWHpTzZp6ER0yqmThxIpdddpnHnaZld345K/cVAlD001v0i/PTOJFwNzed045nRncD4J1lB3jiq+1yrjrhFb7OOIJD1Z8DLSUyQOs4AriqbwIh/j5kF1n5KTNf6zhuye0n1sybN4+NGzeybt26Jt3fZrNhs9mcl8vKyloq2ikdKanihx31b7qOQXayNn1LZuaNLt1GZmamS9sT2rhpcAo+Bj3Tv9zK+6uysNU6eO7KHhj0sruiNblL39FWZWdnY7FYnJc/Wln/e79IBxs3bjyjtqTvOzunev0ubOfLFztreWXRVqJsR07bVmRkJMnJ3nPeOrcumnJycpg8eTI//vgjfn5NG6WZMWMGTz31VAsnO7WyqloWbsnF7lB0iAogxbobgHHjxrXI9ioqKlqkXdF6rh+YjMlHz98+zeCT9TnY6uz885pecoRlK3KHvqOtys7Opkt6OlVWKwDGsHgS7ngb5bDz5K1X8Li1eYe5S993ZsqK6neHnuqzyBAUQcKd/8eOozD40mupyd93yjb9zWZ2ZmZ6TeHk1kXThg0bKCgooG/fvs7r7HY7v/76K6+99ho2mw2DofGS+9OnT2fq1KnOy2VlZSQlJbVa5po6B19tOUJVrZ3oIBOjusWyZcl6AC6781HSevZz2bYy1y7lu/deOeG6WcLz/KVPIr4GA5PnbWL+5iPU2B28fF0ffI1SOLUGrfuOtsxisVBltTJ22gvEJHdgR4mBzDKINeu4+oXZZ9ye9H3NU1VRP3p6us+itRYdOVYYdM9LDIg8+alV8rP3MXfWg1gsFima3MGFF17I1q1bG113yy230KVLF6ZNm3ZcwQRgMpkwmUytFbERpRTfb8+jsKIGs6+By3vG4fO7kYKI+HYkdurmsu3lZ5/6G4DwPJf1jMPXqGfi3I18uzWPmroNvHZjX/x85HxcLU3LvsNbxCR3IKFjV35alQXU0rtDHInNOFG59H1n53SfRcaYaj5Zl8OhKgMjkjoQ5Hfy06d5G7f+ChsUFET37t0b/QQEBBAREUH37t21jnecVfsL2W+pxKDXcXnPOHmjiWYZ2TWGt2/uh8mo56fMAm5/fz3WmjqtYwnhEkdKqymtqsXHoKNDVKDWccQJxAb7kRDqj0PBxuwSreO4FbcumjzJrrxy1h0sBuDCLtGyFpM4K8PTopk9YQBmXwPL9lgY9981lFplwTnh+TJz63cRdYoOajQSL9zLgJT65X22HS6VL22/43Hv2CVLlvDyyy9rHaOR/LJqfjx2eGa/5DDS4858uFmIPxrSMZIPbhtEsJ+RjdklXPf2KgrKZQ6H8Fx1DtiTXz95u6v0k24tOdxMdJCJOodik4w2OXlc0eRuKmx1fL3lCHaHIiXCzJCOcpZu4Tr92oXx6V2DiQoysTOvnGveXEVOkVXrWEI0y5EqPTV2ByH+PsSHyrp17kyn0zEwNRyALYdKsdWefEK4N5Gi6SzU2R0s3HKESpudcLMvF3ePRS+nAhAu1iU2mM/vGkJSuD9ZhVauemMlu/PLtY4lxBk7WFH/kZMeGySnTfEA7SMDiAjwpcbuIONQ85aFaGukaGompRQ/7yogv8yGyajnil5xmIxyhJNoGckRZj67awhpMUEUlNu49q1VbMou1jqWEE1mCI7iqK2+UJIpDJ5Bp9PR/9jcpk05xdTUOTROpD0pmppp6+FSMnPL0QGX9ogj1OyrdSTRxsUE+/HJnefQOymUEmstY/+7hhV7Lad/oBBuILDbnwAdiWH+BPvLkcWeonN0EKH+PlTXOtiUI1/UpGhqhtzSKpburl9ZdWjHSJLDzRonEt4i1OzL3L8OYljHSKw1dm6ZvY5F2/K0jiXEKSmlCOh+ISATwD2NXq/jnPb1c3U3ZpVQ5eVzm6RoOkOVtjq+2ZqLQ0HHqED6JodqHUl4mQCTkf+b0J+Lu8VSY3dwz9wN/G99jtaxhDipnYW1+ITHY9QpOkbL2kyepnNMIJGB9XObNmR592iTFE1nwOFQfLctj0qbnTCzDyO7xshkRqEJk9HAazf24dr+iTgUPPjZFr7eJefhEu7plwP1R3wmmB2yNpMH0ul0DOkQCUBGTgkVNu9dt0nevWdgxT4Lh0uq8DHouLxnvJwTTGjKaNAz66qe3H5uKgCzM8oJOXccSmkcTIjfqbTVsTynfn2xdgEykdhTpUSYiQvxo86hWHugSOs4mpFP/SbanV/uXE5+ZNcYwgNk4rfQnk6n45FL03lwVBoAoUOuJ6PYgJLKSbiJrzOOUF2nqC08RKRJ3peeqn60qX5u0/YjpRRX1micSBtSNDVBcWUNPzWs+N0ujE7RQRonEuI3Op2OiRd05Pa+9RNs91UY+CmzAIcUTsINfLw2G4CKLd8jsxk8W2KYmdTIABwKft1zVOs4mpCi6TTq7A6+2ZZLrV2RGOrPkPay4rdwT5d0DMCy8CVAsSO3jEXb8rA7pHAS2tl+pJSMQ6UY9VCxdbHWcYQLnNsxEr0ODhZayavyvipYiqbTWLr7KIUVNfj7GOpX/NZ735tEeI7K7T9zTmQdeh3sKahg4ZYj1NllHonQxry19Ud1Dkzww1FVpnEa4QphAb70SgwFYEuxEfTetaizFE2nsCuvnG1H6v/RL+4eS4DJqHEiIU4vway4olc8Rr2Og4VWFmQckZV8Rauz1tQxf9NhAEa2l7Xs2pKBqeH4+egpr9MR1PsSreO0KimaTqLYWsPinfXzmAamhMsClsKjpEQEMKZ3Ar4GPYeKq/hy02GqvXxROtG6vtmSS7mtjuRwMz2i5cCZtsTPx8DgY1NVQoaNpaTae/oWKZpOwK7g263185gSQv0Z1D5c60hCnLGEMH/+0jcBP6OevLJqPt94iEovXl9FtK6GCeDXD0ySE5m3Qd3jQwjxcWDwD2LOZu/Z9SpF0wlkFBuw/H4ek/zDCw8VG+zHVf0SMfvWv6c/23iI8uparWOJNm5nXhkbs0sw6nVc3S9R6ziiBej1OvqG21HKwa/Z1SzzkqPppGj6A3OXczlQUT+xbVS3GAJlHpPwcJGBJq7ul0iQn5ESay2fbThEWZUUTqLlNEwAH5EeQ3SQn8ZpREsJNynKNywE4O/zt3nFFAApmn7nSHkdERffB8CAlDDaRQRonEgI1wgz+3JNv0RC/H0oq67js42HKJXCSbSASlsdn288BNTvmhNtW8myDwj315NVaOXfi/doHafFSdF0TJ3dwYuritGbzESaHJyTKusxibYlyM+Hq/smEurvQ3l1/QebFE7C1b7cdJjy6jpSIsyc1ylK6ziihamaKm7vGwLAW7/uZ8uhEm0DtTApmo4xGvSM6RJIbXEuAyPqZD0m0SYF+hm5ql8ioeb6wumzDYcosXrn6RCE6ymleH/VQQBuGpwi/aiXGJTgx+U947A7FPd/srlN76aToul3zk3258h/78JfpjGJNizQZOTqvomEmX2osNXx+cbDUjgJl1i1r5Dd+RWYfQ1c018mgHuTf4zpTnSQiX1HK5n53U6t47QYKZr+yNF2K2QhGgSYjFz1h8KpWAoncZbeOzbKdGXfBIL9fLQNI1pVqNmX56/uCcCclQdZvseicaKWIUWTEF6qoXAKD/A9VjgdksJJNNuhYis/7qhfEPjmwSnahhGaGJ4WzbhzkgF44H8ZFFbYNE7kelI0CeHFAkxGruyTQESAL5U2uxROotk+XJ2NQ8GQDhF0jgnSOo7QyCOXptM+KoC8smqmfLK5zZ00XIomIbxcgMnIlX1/K5y+kDlO4gxV2uqcK4CPH5KibRihKbOvkTfG9sPPR8+yPRZe+3mv1pFcSoomIQRm3/rC6bdddVI4iab7ZF0OpVW1pEYGMCI9Rus4QmNpsUH8Y0wPAF5evLtNzW+SokkIARwrnPokEG7+rXCSdZzE6dTaHfzf8gMA3H5uewyyzIAAru6XyHX9k1AK7vt4I1mFlVpHcgkpmoQQTg276hqOqvtswyEq5By/4hS+2ZLL4ZIqIgN9ubJvgtZxhBt5anQ3eiSEUGyt5dY569rElzApmoQQjfxxOYJl+T4YgqO1jiXckFKKt37dD8CEISn4+Rg0TiTciZ+Pgf+O709ssB/7jlYyce5Gau0OrWOdFbcummbMmMGAAQMICgoiOjqaMWPGsGvXLq1jCdHmNRROoWYfrHYdsTfOoKBShpxEY8v2WMjMLcPsa2DcOe20jiPcUEywH/8d3x9/HwPL91p4fME2lPLcI+rcumhaunQpEydOZPXq1fz444/U1tZy0UUXUVnZNvaNCuHOGgqnQKPCGBLD40uKOFxSpXUs4UbeWLIPgOsHJBNq9tU4jXBX3RNC+PcNfdDp4OO1OcxctNNjCye3LpoWLVrEhAkT6NatG7169WLOnDlkZ2ezYcMGraMJ4RUCTUbOi66ltugIBZV2rn97lRROAqg/Zcqq/YX4GHTcdm6q1nGEmxvZNYbn/lJ/RN1bS/d77FIEHnWWtdLSUgDCw8NPeh+bzYbN9tsqpGVlZS2eS4jmyMzMdOv2GvgbIX/edPo/9CE5RVXc8PZq5t1xDvGh/i2yPa1I39F0Sin+9dNuoH6UKaGNvRfEmWlq35NmhFt6BTE7o5wXf9xNYUEuf04LbHQfm82GyWRyWbbIyEiSk5Nd1p7HFE0Oh4MpU6YwdOhQunfvftL7zZgxg6eeeqoVkwlxZsqKjgIwbty4Fmm/oqLC5W3aywt5engE/1hZQXaRlRveqS+c4kLazoel9B1Nt3JfIWsPFOFr1HPPBR20jiM00ty+LGTI9YSeO445GeW8/NoblK6c97tbdYDrdt35m83szMx0WeHkMUXTxIkT2bZtG8uXLz/l/aZPn87UqVOdl8vKykhKSmrpeEI0WVVF/QjGZXc+SlrPfi5rN3PtUr577xWqq6td1ubvRZoNfHzHOVz/9iqyCq3HRpwGExvi1yLba23SdzSNUoqXfqwfZbpxYHKbKpzFmWluX6YU7CyrY0epkdBzx9H/0hvoGWpn57r6PsxVfWN+9j7mznoQi8XiXUXTvffey8KFC/n1119JTEw85X1NJpNLh/aEaCkR8e1I7NTNZe3lZ+9zWVsnkxDqz8e3n8P1b6/mYOFvI04xwZ5fOEnf0TTL9ljYkFWMyajnnuEyyiSa15clAVE5JSzdfZS95QYMAaFExCY1u73W4tYTwZVS3HvvvXz55Zf8/PPPpKbKZEMhtJYYZubj288hIdSfA5ZKbnh7NfllLTO6JdyLw6H45w/1y77cdE47ottAsSy00zsplIu6xqDTwa68crbQDkNAmNaxTsmti6aJEyfy4Ycf8tFHHxEUFEReXh55eXlUVcnRO0JoKSnczLw76gun/ZZKbnhnNQVSOLV5CzIOs+VQKYEmI3fJKJNwgfS4YMb0TsBk1FOBP7HjX6bM7qN1rJNy66LpjTfeoLS0lOHDhxMXF+f8+eSTT7SOJoTXayic4kP82H/0WOFULoVTW1VVY+f5RfWjTPdc0IHIQNmVKVwjOdzM9QOS8MeGMSiCzbZINmYXu+VaTm5dNCmlTvgzYcIEraMJIWgonAYTF1J/moQb31nD0XLb6R8oPM47y/aTW1pNQqg/tw6VqRLCtULNvvTiIJW7VqDQsWyPhQUZR7DWuNeZCNy6aBJCuL/kiPoRp9hgP/YWVHDjO6uxVEjh1JbkFFl5/Zf6xQgfujhNzjEnWoQRB5b5M+jkU4JBryOr0MoHq7PYmVfmNqNOUjQJIc5au4iAY0fRmdgjhVObopTi8QXbsNU5GNw+gj/3itc6kmjj4n2sXD8gichAX6prHXy/PZ+vMo5QXl2rdTQpmoQQrpESGcC8OwYTE2xid34FY99ZQ6EUTh7v++15/LLrKD4GHc+M6Y5Op9M6kvACkYEmrh+QzOD2ERh0Og4WWvlwdTabc0pwOLQbdZKiSQjhMqmRAXx8+zlEB5nYlV/O2P9K4eTJiitreGzBdgDuPK8DHaMDT/MIIVzHoNcxMDWcGwclExfiR43dwdLdR/loXTaHi7U5il6KJiGES7WPCuTjO84hKsjEzrxyrn5zFTlFVq1jiWb4+4JtHC230Sk6kHv/1FHrOMJLhQf4cnW/RC5Ii8LPqKewoobPNh7iu225VFS37kRxKZqEEC7XISrQuY7TAUslV72xksxcOQGuJ1mw+TDfbMnFqNfx0rW9ZfK30JRep6NnYig3D0mhR0IIALvzK3h/9UHWZxVhb6VddlI0CSFaRIeoQD6/ewhpMUEUlNu49s1VrN5fqHUs0QT7jlbwyBdbAbj3Tx3pkRiicSIh6vn7GPhTl2huGJBEbLAftXbFir2FfLgmi6zCyhbfvhRNQogWExvix6d3DmZgSjjltjpufnct323N1TqWOAVrTR13f7iByho7g1LDufcC2S0n3E90sB/X9k9kZNcY/H0MlFhrmb/5CAu3tOxRdlI0CSFaVIjZh/dvG8hFXWOoqXNwz0cbeWPJPrdZd0X8xuFQPPi/LezOryAqyMSrN/bBaJCPCeGedDodXeOCGT+kHX2SQtHpYN/RSj5YncXG7GJaYo+d/DcIIVqcn4+B/4zty82D26EUzFq0k4c+20JNnUPraOJ3Zi3ayTdbc/Ex6Hjthj5EB8kJeYX7MxkNnNc5ihsG1B9lV2tXLNtj4ec8I77xaS7dlhRNQohWYTToeXp0d576czf0OvjfhkOM+781FFXWaB1NAP+3/ABv/bofgOev7smg9hEaJxLizEQFmbimXyIXdonGZNRTWqsn9NybXboNKZqEEK1q/JAU3p0wgCCTkbUHili5z6J1JK/3f8sP8MzCHQA8cFFn/tInUeNEQjSPTqeje0IINw9uR0qAnaIf33Bp+0aXtiaEaLMyMzNd1lYw8I/hoewuN3J5Tzkth6tkZ2djsTS9CFVK8cXOSuZuLQfgqvQABoeUsXHjRmw2GyaTySW5XPneEaIpzL5G+kXYWVp0yKXtStEkhDilsqKjAIwbN87lbfubzVyXmUlycrLL2/Y22dnZdElPp8raxIVE9QbCL7qHoF6jAChdOY+XZn3IS8476ADXzqStqKhwaXtCtDYpmoQQp1RVUb8o5WV3Pkpaz34uazc/ex9zZz2IxWKRoskFLBYLVVYrY6e9QExyh1Pet7IO1lqMFNXoAUWvMDsdr78Srr8SgMy1S/nuvVdc9jdvaK+6uvqs2xJCS1I0CSGaJCK+HYmdumkdQ5xGTHKHk/6dlFJk5pWzdPdRauoc+Br1XNwtltTIgEb3y8/eB7jub97QnhCeToomIYTwApYKG0t3H+XQsROdxgb7cUn3WIL9fTROJoTnkKJJCCHasGJrDesOFJGZVz/Z23jszPF9k8Mw6HUapxPCs0jRJIQQbYxSisMlVWzKLmG/5bfzcXWMCmRYp0hCZHRJiGaRokkIIdoIQ3AUO0v1LF6dRYn1t/NvpUYGMDA1nNhgWeFbiLMhRZMQQni4H7bn8e9fCkm8ezbbSwFqMep1pMcF0ycplLAAX60jCtEmSNEkhBAeLjO3nG1Ha1DKQbQf9O4QR8eoQHyNctIHIVxJ/qOEEMLDXdk3gbE9gjj8xm2cF1NH17hgKZiEaAHyXyWEEB4uKdzMVemB2MuPah1FiDZNiiYhhBBCiCaQokkIIYQQogmkaBJCCCGEaAIpmoQQQgghmsAjiqbXX3+dlJQU/Pz8GDRoEGvXrtU6khBCCCG8jNsXTZ988glTp07liSeeYOPGjfTq1YtRo0ZRUFCgdTQhhBBCeBG3L5peeuklbr/9dm655Ra6du3Km2++idls5t1339U6mhBCCCG8iFsXTTU1NWzYsIERI0Y4r9Pr9YwYMYJVq1ZpmEwIIYQQ3satT6NisViw2+3ExMQ0uj4mJoadO3ee8DE2mw2bzea8XFpaCkBZWdlpt1dRUQHAoT3bsVVZmxv7OPnZ+wDIO7ibfQFmaVfalXaBo4cOAPX/d035/zwTQUFB6HS6M3rM2fQd7sCV/Zer/+bu3J47Z3N1e+6crSXaa04fc9q+Q7mxw4cPK0CtXLmy0fUPPvigGjhw4Akf88QTTyhAfuRHfrz4p7S09Iz7G+k75Ed+5Od0fYdOKaVwUzU1NZjNZj777DPGjBnjvH78+PGUlJSwYMGC4x7zx2+LDoeDoqIiIiIijqsey8rKSEpKIicnh+Dg4BZ7Hq7iSXk9KSt4Vl5Pygra5HXFSNOp+g7wrL+DJ2UFz8rrSVnBs/K6Y9/h1rvnfH196devH4sXL3YWTQ6Hg8WLF3Pvvfee8DEmkwmTydToutDQ0FNuJzg42O3fPL/nSXk9KSt4Vl5Pygrun7c5fQe4//P6PU/KCp6V15Oygmfldaesbl00AUydOpXx48fTv39/Bg4cyMsvv0xlZSW33HKL1tGEEEII4UXcvmi67rrrOHr0KI8//jh5eXn07t2bRYsWHTc5XAghhBCiJbl90QRw7733nnR33NkwmUw88cQTxw3JuytPyutJWcGz8npSVvC8vE3lSc/Lk7KCZ+X1pKzgWXndMatbTwQXQgghhHAXbr24pRBCCCGEu5CiSQghhBCiCaRoEkIIIYRoAq8uml5//XVSUlLw8/Nj0KBBrF27VutIx5kxYwYDBgwgKCiI6OhoxowZw65du7SO1SQzZ85Ep9MxZcoUraOc1OHDhxk3bhwRERH4+/vTo0cP1q9fr3WsE7Lb7Tz22GOkpqbi7+9Phw4deOaZZ3CHaYm//vorV1xxBfHx8eh0OubPn9/odqUUjz/+OHFxcfj7+zNixAj27NmjTVgX8IS+A6T/aGme0n+4c98BntV/eG3R9MknnzB16lSeeOIJNm7cSK9evRg1ahQFBQVaR2tk6dKlTJw4kdWrV/Pjjz9SW1vLRRddRGVlpdbRTmndunW89dZb9OzZU+soJ1VcXMzQoUPx8fHhu+++Y8eOHbz44ouEhYVpHe2EZs2axRtvvMFrr71GZmYms2bN4vnnn+fVV1/VOhqVlZX06tWL119//YS3P//88/z73//mzTffZM2aNQQEBDBq1Ciqq6tbOenZ85S+A6T/aEme1H+4c98BHtZ/nPEJmtqIgQMHqokTJzov2+12FR8fr2bMmKFhqtMrKChQgFq6dKnWUU6qvLxcderUSf3444/q/PPPV5MnT9Y60glNmzZNDRs2TOsYTXbZZZepW2+9tdF1V155pRo7dqxGiU4MUF9++aXzssPhULGxseqFF15wXldSUqJMJpP6+OOPNUh4djy171BK+g9X8qT+w1P6DqXcv//wypGmmpoaNmzYwIgRI5zX6fV6RowYwapVqzRMdnoNZ14PDw/XOMnJTZw4kcsuu6zR6+uOvvrqK/r3788111xDdHQ0ffr04Z133tE61kkNGTKExYsXs3v3bgAyMjJYvnw5l1xyicbJTu3AgQPk5eU1ej+EhIQwaNAgt/9/+yNP7jtA+g9X8qT+w1P7DnC//sMjFrd0NYvFgt1uP25V8ZiYGHbu3KlRqtNzOBxMmTKFoUOH0r17d63jnNC8efPYuHEj69at0zrKae3fv5833niDqVOn8sgjj7Bu3TomTZqEr68v48eP1zrecR5++GHKysro0qULBoMBu93Os88+y9ixY7WOdkp5eXkAJ/x/a7jNU3hq3wHSf7iaJ/Ufntp3gPv1H15ZNHmqiRMnsm3bNpYvX651lBPKyclh8uTJ/Pjjj/j5+Wkd57QcDgf9+/fnueeeA6BPnz5s27aNN9980+06PYBPP/2UuXPn8tFHH9GtWzc2b97MlClTiI+Pd8u8wr1I/+FantR/SN/hOl65ey4yMhKDwUB+fn6j6/Pz84mNjdUo1ande++9LFy4kF9++YXExESt45zQhg0bKCgooG/fvhiNRoxGI0uXLuXf//43RqMRu92udcRG4uLi6Nq1a6Pr0tPTyc7O1ijRqT344IM8/PDDXH/99fTo0YObbrqJ+++/nxkzZmgd7ZQa/qc86f/tZDyx7wDpP1qCJ/Ufntp3gPv1H15ZNPn6+tKvXz8WL17svM7hcLB48WIGDx6sYbLjKaW49957+fLLL/n5559JTU3VOtJJXXjhhWzdupXNmzc7f/r378/YsWPZvHkzBoNB64iNDB069LjDr3fv3k27du00SnRqVqsVvb7xv6zBYMDhcGiUqGlSU1OJjY1t9P9WVlbGmjVr3O7/7XQ8qe8A6T9akif1H57ad4Ab9h+tPvXcTcybN0+ZTCY1Z84ctWPHDnXHHXeo0NBQlZeXp3W0Ru6++24VEhKilixZonJzc50/VqtV62hN4s5Hv6xdu1YZjUb17LPPqj179qi5c+cqs9msPvzwQ62jndD48eNVQkKCWrhwoTpw4ID64osvVGRkpHrooYe0jqbKy8vVpk2b1KZNmxSgXnrpJbVp0yaVlZWllFJq5syZKjQ0VC1YsEBt2bJFjR49WqWmpqqqqiqNk585T+k7lJL+oyV5Uv/hzn2HUp7Vf3ht0aSUUq+++qpKTk5Wvr6+auDAgWr16tVaRzoOcMKf2bNnax2tSdy501NKqa+//lp1795dmUwm1aVLF/X2229rHemkysrK1OTJk1VycrLy8/NT7du3V48++qiy2WxaR1O//PLLCd+n48ePV0rVHzb82GOPqZiYGGUymdSFF16odu3apW3os+AJfYdS0n+0NE/pP9y571DKs/oPnVJusiSoEEIIIYQb88o5TUIIIYQQZ0qKJiGEEEKIJpCiSQghhBCiCaRoEkIIIYRoAimahBBCCCGaQIomIYQQQogmkKJJCCGEEKIJpGgSQgghhGgCKZrc1JIlS9DpdJSUlGgdRTRRSkoKL7/8stYxhJeTvsPzSN/hOaRochPDhw9nypQpzstDhgwhNzeXkJAQ7UK5gYMHD6LT6di8ebPWUZzmzJlDaGio1jGEAKTvOBnpO0RLMGodQJyYr68vsbGxWscQQngY6TuEaDky0uQGJkyYwNKlS3nllVfQ6XTodDrmzJnTaIi94RvKwoULSUtLw2w2c/XVV2O1WnnvvfdISUkhLCyMSZMmYbfbnW3bbDYeeOABEhISCAgIYNCgQSxZsqRJubKysrjiiisICwsjICCAbt268e233wK/7QL45ptv6NmzJ35+fpxzzjls27atURvLly/n3HPPxd/fn6SkJCZNmkRlZaXz9pSUFJ577jluvfVWgoKCSE5O5u2333benpqaCkCfPn3Q6XQMHz78tLkdDgdPP/00iYmJmEwmevfuzaJFi5y3N3wD/eKLL7jgggswm8306tWLVatWnbbtJUuWcMstt1BaWur8Wz355JPO261W60mfC0BOTg7XXnstoaGhhIeHM3r0aA4ePHja7UL9+2TMmDE89dRTREVFERwczF133UVNTY3zPosWLWLYsGGEhoYSERHB5Zdfzr59+xq1s3LlSnr37o2fnx/9+/dn/vz5x30j37ZtG5dccgmBgYHExMRw0003YbFYmpRTtB7pO6TvaArpO1xIk9MEi0ZKSkrU4MGD1e23365yc3NVbm6u+umnnxSgiouLlVJKzZ49W/n4+KiRI0eqjRs3qqVLl6qIiAh10UUXqWuvvVZt375dff3118rX11fNmzfP2fZf//pXNWTIEPXrr7+qvXv3qhdeeEGZTCa1e/fu0+a67LLL1MiRI9WWLVvUvn371Ndff62WLl2qlPrtrNTp6enqhx9+UFu2bFGXX365SklJUTU1NUoppfbu3asCAgLUv/71L7V79261YsUK1adPHzVhwgTnNtq1a6fCw8PV66+/rvbs2aNmzJih9Hq92rlzp1JKqbVr1ypA/fTTTyo3N1cVFhaeNvdLL72kgoOD1ccff6x27typHnroIeXj4+N8zgcOHFCA6tKli1q4cKHatWuXuvrqq1W7du1UbW3tKdu22Wzq5ZdfVsHBwc6/VXl5eZOeS01NjUpPT1e33nqr2rJli9qxY4e68cYbVVpaWpPONj5+/HgVGBiorrvuOrVt2za1cOFCFRUVpR555BHnfT777DP1+eefqz179qhNmzapK664QvXo0UPZ7XallFKlpaUqPDxcjRs3Tm3fvl19++23qnPnzgpQmzZtUkopVVxcrKKiotT06dNVZmam2rhxoxo5cqS64IILTptRtC7pO6TvkL6jdUnR5CbOP/98NXnyZOflho7l9x0foPbu3eu8z5133qnMZrPzH08ppUaNGqXuvPNOpZRSWVlZymAwqMOHDzfa1oUXXqimT59+2kw9evRQTz755Alva8j3+062sLBQ+fv7q08++UQppdRtt92m7rjjjkaPW7ZsmdLr9aqqqkopVd9ZjBs3znm7w+FQ0dHR6o033lBK/dZJNfxTNkV8fLx69tlnG103YMAAdc899zRq87///a/z9u3btytAZWZmnrb92bNnq5CQkOOuP91z+eCDD1RaWppyOBzO+9hsNuXv76++//770253/PjxKjw8XFVWVjqve+ONN1RgYKCzY/ujo0ePKkBt3brVef+IiAjn66+UUu+8806j1/iZZ55RF110UaN2cnJyFKB27dp12pyidUnfUU/6jpOTvsN1ZE6TBzGbzXTo0MF5OSYmhpSUFAIDAxtdV1BQAMDWrVux2+107ty5UTs2m42IiIjTbm/SpEncfffd/PDDD4wYMYKrrrqKnj17NrrP4MGDnb+Hh4eTlpZGZmYmABkZGWzZsoW5c+c676OUwuFwcODAAdLT0wEatanT6YiNjXU+hzNVVlbGkSNHGDp0aKPrhw4dSkZGRqPrfr/duLg4AAoKCujSpUuztv3HNv/4XDIyMti7dy9BQUGNHlNdXX3cMPjJ9OrVC7PZ7Lw8ePBgKioqyMnJoV27duzZs4fHH3+cNWvWYLFYcDgcAGRnZ9O9e3d27drl3CXSYODAgY22kZGRwS+//NLofdVg3759x72fhPuTvuP0pO+QvqMppGjyID4+Po0u63S6E17X8GavqKjAYDCwYcMGDAZDo/ud6E39R3/9618ZNWoU33zzDT/88AMzZszgxRdf5L777mtS3oqKCu68804mTZp03G3JycmnfF4Nz6El/X67Op0O4Ky3e7q/R79+/Rp9EDSIioo6q+02uOKKK2jXrh3vvPMO8fHxOBwOunfv3mjuwulUVFRwxRVXMGvWrONua/iAEJ5F+g7Xkr7jxLyh75CiyU34+vo2moTpCn369MFut1NQUMC5557brDaSkpK46667uOuuu5g+fTrvvPNOo45v9erVzk6suLiY3bt3O78F9u3blx07dtCxY8dmPwdfX1+AJr82wcHBxMfHs2LFCs4//3zn9StWrDjuW9HZZGrO36pv37588sknREdHExwc3KxtZ2RkUFVVhb+/P1D/+gcGBpKUlERhYSG7du3inXfecf69ly9f3ujxaWlpfPjhh9hsNkwmEwDr1q07Lufnn39OSkoKRqN0Ee5O+o4Tk76jMek7XEOOnnMTKSkprFmzhoMHDzYaGj0bnTt3ZuzYsdx888188cUXHDhwgLVr1zJjxgy++eab0z5+ypQpfP/99xw4cICNGzfyyy+/ODu1Bk8//TSLFy9m27ZtTJgwgcjISMaMGQPAtGnTWLlyJffeey+bN29mz549LFiwgHvvvbfJzyE6Ohp/f38WLVpEfn4+paWlp33Mgw8+yKxZs/jkk0/YtWsXDz/8MJs3b2by5MlN3u6ppKSkUFFRweLFi7FYLFit1iY9buzYsURGRjJ69GiWLVvGgQMHWLJkCZMmTeLQoUNNaqOmpobbbruNHTt28O233/LEE09w7733otfrCQsLIyIigrfffpu9e/fy888/M3Xq1EaPv/HGG3E4HNxxxx1kZmby/fff889//hP47RvzxIkTKSoq4oYbbmDdunXs27eP77//nltuucXlH87i7EnfcWLSdzQmfYdrSNHkJh544AEMBgNdu3YlKiqK7Oxsl7Q7e/Zsbr75Zv72t7+RlpbGmDFjWLduXaMh7pOx2+1MnDiR9PR0Lr74Yjp37sx//vOfRveZOXMmkydPpl+/fuTl5fH11187v+H17NmTpUuXsnv3bs4991z69OnD448/Tnx8fJPzG41G/v3vf/PWW28RHx/P6NGjT/uYSZMmMXXqVP72t7/Ro0cPFi1axFdffUWnTp2avN1TGTJkCHfddRfXXXcdUVFRPP/88016nNls5tdffyU5OZkrr7yS9PR0brvtNqqrq5v87fHCCy+kU6dOnHfeeVx33XX8+c9/dh62rNfrmTdvHhs2bKB79+7cf//9vPDCC40eHxwczNdff83mzZvp3bs3jz76KI8//jiAc65Cw7dtu93ORRddRI8ePZgyZQqhoaHo9dJluBvpO05M+o7GpO9wDZ1SSmkdQnieJUuWcMEFF1BcXCwr3LaSCRMmUFJSwvz5813a7ty5c53rxzQM3QvRUqTvaH3Sd7hO29zpKIQ4qffff5/27duTkJBARkYG06ZN49prr/WaTk8I0TzSd8juOa/WsGrriX6ee+45reOd1MkyBwYGsmzZsrNuX6vXpaWfV4O8vDzGjRtHeno6999/P9dcc81xqw8LcSrSd5yY9B1tn+ye82KHDx+mqqrqhLeFh4cTHh7eyomaZu/evSe9LSEh4ay/9Wj1urT08xLCVaTvODHpO9o+KZqEEEIIIZpAds8JIYQQQjSBFE1CCCGEEE0gRZMQQgghRBNI0SSEEEII0QRSNAkhhBBCNIEUTUIIIYQQTSBFkxBCCCFEE0jRJIQQQgjRBP8P+T+AYlr+R/gAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* The old landing page is slightly right skewed. 50% of users spend between 3 and 6 minutes. The median time that users in the old page spend is a little more than 4 minutes.\n",
        "* The new landing page has a few outliers on both ends. 50% percent of the users spend between 5 and 7 minutes on the page. The median of time spent on the page by users on the new page is around 6 minutes."
      ],
      "metadata": {
        "id": "OxbW1vT1lBjS"
      },
      "id": "OxbW1vT1lBjS"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "56b3dafd"
      },
      "source": [
        "### Step 1: Define null and alternate hypotheses"
      ],
      "id": "56b3dafd"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d2ac77ef"
      },
      "source": [
        "Null hypothesis: The mean time spent on both landing pages is equal.\n",
        "$H_0:\\mu_1=\\mu_2$\n",
        "\n",
        "Alternate hypothesis: The mean time spent on the new landing page ($\\mu_1$)  is greater than the old landing page ($\\mu_2$).\n",
        "$H_a:\\mu_1>\\mu_2$"
      ],
      "id": "d2ac77ef"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c7ee4907"
      },
      "source": [
        "### Step 2: Select appropriate test"
      ],
      "id": "c7ee4907"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2c183cd7"
      },
      "source": [
        "\n",
        "We have the following information from the data:\n",
        "* Continuous data: Time (in minutes) is measured on a continuous scale.\n",
        "* Normal distribution: By looking at the visuals, we can assume that it is normally distributed.\n",
        "* Independent populations: The two samples are from two independent populations (new and old landing pages).\n",
        "* Unknown population standard deviations: We will need to calculate standard deviations to calculate the p-value.\n",
        "* Random sampling from the population.\n",
        "* We are comparing means.\n",
        "* One-tailed test: we are testing if time is greater on the new page than the old page.\n",
        "\n",
        "**We will use a 2 sample independent t-test.**"
      ],
      "id": "2c183cd7"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3f58c9a7"
      },
      "source": [
        "### Step 3: Decide the significance level"
      ],
      "id": "3f58c9a7"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "92d7d7ee"
      },
      "source": [
        "We'll set the significance level (alpha) as 0.05"
      ],
      "id": "92d7d7ee"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fed2eac"
      },
      "source": [
        "### Step 4: Collect and prepare data"
      ],
      "id": "5fed2eac"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5a59495"
      },
      "source": [
        "# Creating separate variables to store the new page and the old page information\n",
        "new_page = data[data['landing_page']=='new']\n",
        "old_page = data[data['landing_page']=='old']"
      ],
      "id": "f5a59495",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the standard deviation for the time spent on the new page\n",
        "new_page['time_spent_on_the_page'].std()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5j8HC-0zxNxu",
        "outputId": "10947e52-be06-426b-d2db-01f20aa9192d"
      },
      "id": "5j8HC-0zxNxu",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.8170310387878263"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the standard deviation for the time spent on the old page\n",
        "old_page['time_spent_on_the_page'].std()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DkpT-FMoxTE-",
        "outputId": "63aba57f-ff49-453b-da4c-f7408d91efdd"
      },
      "id": "DkpT-FMoxTE-",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.581974849306046"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* The standard deviations are unequal.\n",
        "* Standard deviation for the time spent on the new page is 1.82 and 2.58 on the old page."
      ],
      "metadata": {
        "id": "4RvDTEAJxp62"
      },
      "id": "4RvDTEAJxp62"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f5c5258d"
      },
      "source": [
        "### Step 5: Calculate the p-value"
      ],
      "id": "f5c5258d"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fac8594a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51c72ab7-5bbe-422a-b248-e09a099bfdd6"
      },
      "source": [
        "# Importing the function to calculate p-value\n",
        "from scipy.stats import ttest_ind\n",
        "\n",
        "# Finding the p-value\n",
        "test_stat, p_value = ttest_ind(new_page['time_spent_on_the_page'], old_page['time_spent_on_the_page'], equal_var = False, alternative = 'greater')\n",
        "print('The p-value is', str(p_value))"
      ],
      "id": "fac8594a",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The p-value is 0.0001392381225166549\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "359b12f8"
      },
      "source": [
        "### Step 6: Compare the p-value with alpha ($\\alpha$)"
      ],
      "id": "359b12f8"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7be47289",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a362377-d5fc-4e7b-b497-acc191f13282"
      },
      "source": [
        "# Conclusion based on p-value\n",
        "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.')"
      ],
      "id": "7be47289",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "As the p-value 0.0001392381225166549 is less than the level of significance, we reject the null hypothesis.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9affc84"
      },
      "source": [
        "### Step 7:  Draw inference"
      ],
      "id": "d9affc84"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6b16dd4b"
      },
      "source": [
        "Since the p-value is much smaller (0.000139) than the significance level (alpha) of 5%, we reject the null hypothesis. We have enough statistical evidence to conclude that the mean time spent on the new page is greater than the mean time spent on the old landing page."
      ],
      "id": "6b16dd4b"
    },
    {
      "cell_type": "markdown",
      "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?"
      ],
      "id": "353e9d24"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visual analysis"
      ],
      "metadata": {
        "id": "Z3H-RHpj5QgQ"
      },
      "id": "Z3H-RHpj5QgQ"
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b7f52898",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "outputId": "0179381c-a5c2-485a-cd7e-52ceeb87fceb"
      },
      "source": [
        "# Visual on correlation between landing page and converted\n",
        "sns.countplot(data=data,x='landing_page',hue='converted');"
      ],
      "id": "b7f52898",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# To create a dataset where only the rows that contain new are included from from the landing page column\n",
        "new_page=data[data['landing_page']=='new']\n",
        "\n",
        "# Getting the conversion percentage of users in the new landing page\n",
        "new_page['converted'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3peHAOe5tok",
        "outputId": "5b6ce17d-b26b-4168-d44b-f2344c68660f"
      },
      "id": "R3peHAOe5tok",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "converted\n",
              "yes    0.66\n",
              "no     0.34\n",
              "Name: proportion, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# To create a dataset where only the rows that contain old are included from the landing page column\n",
        "old_page=data[data['landing_page']=='old']\n",
        "\n",
        "# Getting the conversion percentage of users in the old landing page\n",
        "old_page['converted'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L5yuw0dv5owN",
        "outputId": "40dbc736-bed4-4fea-8263-e8144f42e84e"
      },
      "id": "L5yuw0dv5owN",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "converted\n",
              "no     0.58\n",
              "yes    0.42\n",
              "Name: proportion, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a contingency table to tabulate the frequencies of the two categorical variables\n",
        "pd.crosstab(data.landing_page,data.converted,margins=True)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
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        "outputId": "d00ec086-c653-4730-a89d-5ae00998e055"
      },
      "id": "OY64ZknACFBE",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "converted     no  yes  All\n",
              "landing_page              \n",
              "new           17   33   50\n",
              "old           29   21   50\n",
              "All           46   54  100"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"landing_page\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"new\",\n          \"old\",\n          \"All\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"no\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 17,\n        \"max\": 46,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          17,\n          29,\n          46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"yes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 21,\n        \"max\": 54,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          33,\n          21,\n          54\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"All\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28,\n        \"min\": 50,\n        \"max\": 100,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          100,\n          50\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* 33 out of 50 users in the new landing page subscribed to the news portal. Comprising 66% of the users.\n",
        "* 21 out of the 50 users of the old landing page converted to a subscriber. 42% of the old page users subscribed."
      ],
      "metadata": {
        "id": "OCzRcQlA58rc"
      },
      "id": "OCzRcQlA58rc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 1: Define null and alternate hypothesis"
      ],
      "metadata": {
        "id": "_ItLeTFK6tsE"
      },
      "id": "_ItLeTFK6tsE"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Null hypothesis ($H_0):$ The proportion of subscribers in the new landing page is equal to the proportion of subscribers in the old landing page. p1 = p2\n",
        "\n",
        "Alternate hypothesis ($H_a):$ The proportion of subscribers on the new landing page is greater than the old landing page. p1 > p2"
      ],
      "metadata": {
        "id": "EacJ7_oK62lC"
      },
      "id": "EacJ7_oK62lC"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 2: Select appropriate test"
      ],
      "metadata": {
        "id": "TgwzuUqd7Uuk"
      },
      "id": "TgwzuUqd7Uuk"
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above hypothesis concerns proportions of 2 categorical variables (landing page and conversion). It is a binomally distributed proportion (users convert or don't).This is a one-tail test from 2 population proportions and 2 different populations where the sample size is greater than 30.\n",
        "\n",
        "**We will use a 2 proportion z-test**"
      ],
      "metadata": {
        "id": "vyNSyj8a7YvX"
      },
      "id": "vyNSyj8a7YvX"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 3: Decide the significance level"
      ],
      "metadata": {
        "id": "_E6ppK99-fWJ"
      },
      "id": "_E6ppK99-fWJ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll set the significance level (alpha) as 0.05"
      ],
      "metadata": {
        "id": "cnJWk8VO-jCc"
      },
      "id": "cnJWk8VO-jCc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 4: Collect and prepare data"
      ],
      "metadata": {
        "id": "Flj66DgW-tvZ"
      },
      "id": "Flj66DgW-tvZ"
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the number of converted users in the new page using the previously created variable of new_page\n",
        "new_converted = new_page.converted.value_counts()['yes']\n",
        "\n",
        "# Number of converted users in the old landing page using the previously created variable old_page\n",
        "old_converted = old_page.converted.value_counts()['yes']\n",
        "\n",
        "print('The numbers of converted users in the new and old landing pages are {0} and {1} respectively'.format(new_converted, old_converted))\n",
        "\n",
        "# Total number of users in the new page\n",
        "new_users = new_page.landing_page.value_counts()['new']\n",
        "\n",
        "# Total number of users in the old page\n",
        "old_users = old_page.landing_page.value_counts()['old']\n",
        "\n",
        "print('The total numbers of new landing page users and old page users are {0} and {1} respectively'.format(new_users, old_users))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZILWptFq8eW",
        "outputId": "0852f286-89f7-45e8-c698-a321c129c1a7"
      },
      "id": "hZILWptFq8eW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The numbers of converted users in the new and old landing pages are 33 and 21 respectively\n",
            "The total numbers of new landing page users and old page users are 50 and 50 respectively\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f' The proportions of users in new and old landing pages are {round(33/50,2)}, {round(21/50,2)} respectively')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T9NDPeD19sAd",
        "outputId": "ab5f9faf-df3a-4612-d407-5dad2171ff23"
      },
      "id": "T9NDPeD19sAd",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " The proportions of users in new and old landing pages are 0.66, 0.42 respectively\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* The proportions are different. We will conduct the test to see if the difference is significant."
      ],
      "metadata": {
        "id": "w-n6Uh0z_gEN"
      },
      "id": "w-n6Uh0z_gEN"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 5: Calculate the p-value"
      ],
      "metadata": {
        "id": "QmaGCP9Q9jHM"
      },
      "id": "QmaGCP9Q9jHM"
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the necessary function\n",
        "from statsmodels.stats.proportion import proportions_ztest\n",
        "\n",
        "# Find the p-value using proportions_ztest\n",
        "stat, pval = proportions_ztest([new_converted, old_converted] , [new_users, old_users], alternative = 'larger')\n",
        "\n",
        "# Print the p-value\n",
        "print('The p-value is '+ str(pval))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nr-4n2fH932e",
        "outputId": "b948a357-7a71-43e5-800f-3730089451e2"
      },
      "id": "nr-4n2fH932e",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The p-value is 0.008026308204056278\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 6: Compare the p-value with alpha ($\\alpha$)"
      ],
      "metadata": {
        "id": "avWU-po__t9z"
      },
      "id": "avWU-po__t9z"
    },
    {
      "cell_type": "code",
      "source": [
        "# Printing the conclusion based on p-value\n",
        "if pval < 0.05:\n",
        "    print(f'As the p-value {pval} is less than the level of significance, we reject the null hypothesis.')\n",
        "else:\n",
        "    print(f'As the p-value {pval} is greater than the level of significance, we fail to reject the null hypothesis.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sHh5p1e3_9iD",
        "outputId": "f8dfa07e-eb1f-4460-97d3-336166e8d0ac"
      },
      "id": "sHh5p1e3_9iD",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "As the p-value 0.008026308204056278 is less than the level of significance, we reject the null hypothesis.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 7: Draw inference"
      ],
      "metadata": {
        "id": "fWho8lgtAI4j"
      },
      "id": "fWho8lgtAI4j"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since the p-value is less than the significance level (0.05), we reject the null hypothesis. Hence, we have enough statistical evidence to say that the proportion of converted users is greater in the new page than the old page."
      ],
      "metadata": {
        "id": "1KLaBVp6AQYs"
      },
      "id": "1KLaBVp6AQYs"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d49bfa2d"
      },
      "source": [
        "## 3. Are the conversion and preferred language independent or related?"
      ],
      "id": "d49bfa2d"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visual analysis"
      ],
      "metadata": {
        "id": "AR1LlFf_A5s8"
      },
      "id": "AR1LlFf_A5s8"
    },
    {
      "cell_type": "code",
      "source": [
        "# Visual between language preferred and landing page\n",
        "sns.countplot(data=data,hue='converted', x='language_preferred');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "EmIAE4lVCya6",
        "outputId": "285b02aa-a515-4b56-8982-4dd9c8419c30"
      },
      "id": "EmIAE4lVCya6",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b0e63f17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "989512b1-2a9e-4cf4-833b-41dcdc27366c"
      },
      "source": [
        "# Create a contingency table to tabulate the frequencies of the two categorical variables\n",
        "pd.crosstab(data.converted,data.language_preferred,margins=True)"
      ],
      "id": "b0e63f17",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "language_preferred  English  French  Spanish  All\n",
              "converted                                        \n",
              "no                       11      19       16   46\n",
              "yes                      21      15       18   54\n",
              "All                      32      34       34  100"
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th>language_preferred</th>\n",
              "      <th>English</th>\n",
              "      <th>French</th>\n",
              "      <th>Spanish</th>\n",
              "      <th>All</th>\n",
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              "      <th>no</th>\n",
              "      <td>11</td>\n",
              "      <td>19</td>\n",
              "      <td>16</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yes</th>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
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              "      <td>54</td>\n",
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              "      <td>32</td>\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        document.querySelector('#df-d9a68771-57fc-4243-82ad-94023a11109a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d9a68771-57fc-4243-82ad-94023a11109a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "  .colab-df-quickchart-complete:disabled:hover {\n",
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              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    80% {\n",
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              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"converted\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"no\",\n          \"yes\",\n          \"All\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"English\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 11,\n        \"max\": 32,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          11,\n          21,\n          32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"French\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10,\n        \"min\": 15,\n        \"max\": 34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          19,\n          15,\n          34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spanish\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 16,\n        \"max\": 34,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          16,\n          18,\n          34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"All\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 46,\n        \"max\": 100,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          46,\n          54,\n          100\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Looking at the percentages of the converted users with English as the preferred language\n",
        "data[data['language_preferred']=='English'].converted.value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-nAXuko5DNcW",
        "outputId": "3f3018e2-946d-465a-c583-4fa12772856c"
      },
      "id": "-nAXuko5DNcW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "converted\n",
              "yes    0.65625\n",
              "no     0.34375\n",
              "Name: proportion, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Looking at the percentages of the converted users with Spanish as the preferred language\n",
        "data[data['language_preferred']=='Spanish'].converted.value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oL_1RarbEvFZ",
        "outputId": "a11c346c-410b-4849-e3aa-7289319a1ada"
      },
      "id": "oL_1RarbEvFZ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "converted\n",
              "yes    0.529412\n",
              "no     0.470588\n",
              "Name: proportion, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Looking at the percentages of the converted users with French as the preferred language\n",
        "data[data['language_preferred']=='French'].converted.value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IN2y8HCjEx5U",
        "outputId": "10f49832-a1ab-4feb-c14f-9e45717417fa"
      },
      "id": "IN2y8HCjEx5U",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "converted\n",
              "no     0.558824\n",
              "yes    0.441176\n",
              "Name: proportion, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 54 out of the 100 users converted to a subscriber.\n",
        "* English users have the highest conversion rate with 21 out of 32. 66% of the Ensligh users converted to a subscriber.\n",
        "* French users have the lowest conversion rate with only 44% of French users converting, or 15 out of 34.\n",
        "* Around 53% of the Spanish users subscribed to the website. Comprising 18 out of 34 users with Spanish as a preferred language."
      ],
      "metadata": {
        "id": "EuFujyt9C7_S"
      },
      "id": "EuFujyt9C7_S"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 1: Define null and alternate hypothesis"
      ],
      "metadata": {
        "id": "GW04RDN0FeUR"
      },
      "id": "GW04RDN0FeUR"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Null hypothesis ($H_0):$ Converted status and language preferred are independent of each other.\n",
        "\n",
        "Alternate hypothesis ($H_a):$ Converted status and language preferred are dependent on each other."
      ],
      "metadata": {
        "id": "q57hvz_GFxR7"
      },
      "id": "q57hvz_GFxR7"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 2: Select appropriate test"
      ],
      "metadata": {
        "id": "2QBqN6rEGpgO"
      },
      "id": "2QBqN6rEGpgO"
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above formulated hypothesis concerns 2 categorical variables (language preferred and converted status). To test the dependence of two categorical variables we use a **chi-square test of independence**."
      ],
      "metadata": {
        "id": "ibObsyolGwS_"
      },
      "id": "ibObsyolGwS_"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 3: Decide the significance level"
      ],
      "metadata": {
        "id": "7_9W7asjHMJm"
      },
      "id": "7_9W7asjHMJm"
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll set the significance level (alpha) as 0.05"
      ],
      "metadata": {
        "id": "_4OOMs_AHrSY"
      },
      "id": "_4OOMs_AHrSY"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 4: Collect and prepare data"
      ],
      "metadata": {
        "id": "B8YEWS1nHsST"
      },
      "id": "B8YEWS1nHsST"
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a variable for the contingency table previously created\n",
        "contingency_table = pd.crosstab(data.converted,data.language_preferred)\n",
        "contingency_table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "EtksWxGUI6If",
        "outputId": "ad91ed08-0f76-42cd-90fa-8b2dbcbcb5ef"
      },
      "id": "EtksWxGUI6If",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "language_preferred  English  French  Spanish\n",
              "converted                                   \n",
              "no                       11      19       16\n",
              "yes                      21      15       18"
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-98a5e5a9-fdcc-4f61-9318-c76bd4e36a2a button.colab-df-convert');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "        dataTable['output_type'] = 'display_data';\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('contingency_table')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_c5f36a88-a873-494a-8338-b63e533b7c5b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('contingency_table');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "contingency_table",
              "summary": "{\n  \"name\": \"contingency_table\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"converted\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"English\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 11,\n        \"max\": 21,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          21,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"French\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 15,\n        \"max\": 19,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          15,\n          19\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spanish\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 16,\n        \"max\": 18,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          18,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* We have created a contingency table with the counts of converted (yes or no) in rows and the counts for language preferred (English, French, and Spanish) in columns. We will use this table to conduct the test."
      ],
      "metadata": {
        "id": "2H2eHT_TJm2s"
      },
      "id": "2H2eHT_TJm2s"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 5: Calculate the p-value"
      ],
      "metadata": {
        "id": "ZCdKCyRXJUUx"
      },
      "id": "ZCdKCyRXJUUx"
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the required test function\n",
        "from scipy.stats import chi2_contingency\n",
        "\n",
        "# Calculate the test results and printing the values\n",
        "chi, p_value, dof, expected = chi2_contingency(contingency_table)\n",
        "\n",
        "print(\"Test Statistic =\",chi)\n",
        "print(\"The p-value is =\",p_value)\n",
        "print(\"Degrees of freedom =\",dof)\n",
        "print(\"Expected frequencies \\n\", expected)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jpYFgfUHJgzY",
        "outputId": "8b2e80ef-4d96-4b14-e382-0c5a1d8b363e"
      },
      "id": "jpYFgfUHJgzY",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Statistic = 3.0930306905370837\n",
            "The p-value is = 0.21298887487543447\n",
            "Degrees of freedom = 2\n",
            "Expected frequencies \n",
            " [[14.72 15.64 15.64]\n",
            " [17.28 18.36 18.36]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 6: Compare the p-value with alpha ($\\alpha$)"
      ],
      "metadata": {
        "id": "01QOcmtyKpkO"
      },
      "id": "01QOcmtyKpkO"
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the conclusion based on p-value\n",
        "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.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q3OS-HJLK6jv",
        "outputId": "a7b41a4a-58dc-46fd-a52f-37f956c125f9"
      },
      "id": "Q3OS-HJLK6jv",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "As the p-value 0.21298887487543447 is greater than the level of significance, we fail to reject the null hypothesis.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 7: Draw inference"
      ],
      "metadata": {
        "id": "kLyb7JlHK85p"
      },
      "id": "kLyb7JlHK85p"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since the p-value is greater than the level of significance (0.05), we fail to reject the null hypothesis. Hence, there isn't enough statistical evidence to say that converted status and language preferred are dependent on each other."
      ],
      "metadata": {
        "id": "rs0Uurc1LA9T"
      },
      "id": "rs0Uurc1LA9T"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8d585a90"
      },
      "source": [
        "## 4. Is the time spent on the new page same for the different language users?"
      ],
      "id": "8d585a90"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visual analysis"
      ],
      "metadata": {
        "id": "VpCKNntYLwt2"
      },
      "id": "VpCKNntYLwt2"
    },
    {
      "cell_type": "code",
      "source": [
        "# Visual on correlation between time spent on the new page (previously created variable) and language preferred\n",
        "sns.boxplot(data=new_page,x='language_preferred',y='time_spent_on_the_page',showmeans=True)\n",
        "plt.show()\n",
        "g = sns.FacetGrid(new_page, col='language_preferred')\n",
        "g.map(sns.histplot, 'time_spent_on_the_page',kde=True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        },
        "id": "mK9GWwhML0hY",
        "outputId": "cb3eb6e9-1235-4645-ea66-3b1ba8a6b577"
      },
      "id": "mK9GWwhML0hY",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x300 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b422f395",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20a8d365-456b-4a50-8204-f5f0da640310"
      },
      "source": [
        "# Calculating the average time spent by users depending on language preferred\n",
        "print(new_page.groupby('language_preferred')['time_spent_on_the_page'].mean())"
      ],
      "id": "b422f395",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "language_preferred\n",
            "English    6.663750\n",
            "French     6.196471\n",
            "Spanish    5.835294\n",
            "Name: time_spent_on_the_page, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* English users have a similar mean and median, spending an average of 6.66 minutes. They spend between around 4 and 11 minutes on the website.\n",
        "* The distribution of Spanish users is left skewed. Spanish users spend an average of 5.84 minutes on the page. There is one outlier on the lower whisker, but most of the users spend between 4 and 8 minutes on the page.\n",
        "* French users has a right skewed distribution. The average time spent is 6.2 minutes and the median is around 5.5 minutes. There is one outlier on the upper side."
      ],
      "metadata": {
        "id": "WXfWm1KMOsLP"
      },
      "id": "WXfWm1KMOsLP"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 1: Define null and alternate hypothesis"
      ],
      "metadata": {
        "id": "3fqdnx0qQFa0"
      },
      "id": "3fqdnx0qQFa0"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let $\\mu_1, \\mu_2, \\mu_3$ be the means of time spent on the new page for language preferred English, French, and Spanish respectively.\n",
        "\n",
        "$H_0: \\mu_1 = \\mu_2 = \\mu_3$\n",
        "\n",
        "against the alternative hypothesis\n",
        "\n",
        "$H_a: $ At least one of the mean time spent is different from the other languages."
      ],
      "metadata": {
        "id": "EI3nGzOhQScN"
      },
      "id": "EI3nGzOhQScN"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 2: Select appropriate test"
      ],
      "metadata": {
        "id": "sXgSXWFuQvgu"
      },
      "id": "sXgSXWFuQvgu"
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above formulated hypothesis concerns the means of three populations. The variables that we are working with are one categorical and one quatitative. We will use **one way ANOVA** to test if there is any significant difference between them.\n",
        "\n",
        "First, we will need to check for normality and equality of variances."
      ],
      "metadata": {
        "id": "3pp4kKguRfNc"
      },
      "id": "3pp4kKguRfNc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Shapiro-Wilk's test of normality"
      ],
      "metadata": {
        "id": "JikycWwpSJno"
      },
      "id": "JikycWwpSJno"
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will test the null hypothesis\n",
        "\n",
        "$H_0:$ The time spent follows a normal distribution\n",
        "\n",
        "against the alternative hypothesis\n",
        "\n",
        "$H_a:$ The time spent does not not follow a normal distribution"
      ],
      "metadata": {
        "id": "2TeCEKH-SZ1W"
      },
      "id": "2TeCEKH-SZ1W"
    },
    {
      "cell_type": "code",
      "source": [
        "# Assumption 1: Normality\n",
        "# Import the required function\n",
        "from scipy import stats\n",
        "\n",
        "# Find the p-value\n",
        "w, p_value = stats.shapiro(new_page['time_spent_on_the_page'])\n",
        "print('The p-value is', p_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TmtT1p5xSr4-",
        "outputId": "485f4634-538b-4d9a-9a28-e331029ad50d"
      },
      "id": "TmtT1p5xSr4-",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The p-value is 0.8040016293525696\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Since the p-value is much larger than the significance level of 5%, we fail to reject the null hypothesis that it follows a normal distribution."
      ],
      "metadata": {
        "id": "50_UPzr9S9wD"
      },
      "id": "50_UPzr9S9wD"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Levene's test for equality of variance"
      ],
      "metadata": {
        "id": "2rt0_iFRUQVv"
      },
      "id": "2rt0_iFRUQVv"
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will test the null hypothesis\n",
        "\n",
        "$H_0$: All the population variances are equal\n",
        "\n",
        "against the alternative hypothesis\n",
        "\n",
        "$H_a$: At least one variance is different from the rest"
      ],
      "metadata": {
        "id": "tZg01XCcUnCD"
      },
      "id": "tZg01XCcUnCD"
    },
    {
      "cell_type": "code",
      "source": [
        "# Assumption 2: Homogeneity of Variance\n",
        "# Import the required function\n",
        "from scipy.stats import levene\n",
        "\n",
        "# Find the p-value\n",
        "statistic, p_value = stats.levene(new_page[new_page['language_preferred']=='English']['time_spent_on_the_page'],\n",
        "                            new_page[new_page['language_preferred']=='French']['time_spent_on_the_page'],\n",
        "                            new_page[new_page['language_preferred']=='Spanish']['time_spent_on_the_page'])\n",
        "# Print the p-value\n",
        "print('The p-value is', p_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kaSOud0-UqBK",
        "outputId": "9a4072ef-6884-4395-af19-b23a62bf6c13"
      },
      "id": "kaSOud0-UqBK",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The p-value is 0.46711357711340173\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Since the p-value is greater than the significance level of 5%, we fail to reject the null hypothesis of homogeneity of variances."
      ],
      "metadata": {
        "id": "HMCjRfo9WR7s"
      },
      "id": "HMCjRfo9WR7s"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 3: Decide the significance level"
      ],
      "metadata": {
        "id": "n2qLyT9wWjlt"
      },
      "id": "n2qLyT9wWjlt"
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll set the significance level (alpha) as 0.05"
      ],
      "metadata": {
        "id": "xavqs1nAWrGx"
      },
      "id": "xavqs1nAWrGx"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 4: Collect and prepare data"
      ],
      "metadata": {
        "id": "ca3JGXWXWtA4"
      },
      "id": "ca3JGXWXWtA4"
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating separate variables to store the time spent on the new page with respect to the three languages preferred\n",
        "time_english = new_page[new_page['language_preferred']=='English']['time_spent_on_the_page']\n",
        "time_french = new_page[new_page['language_preferred']=='French']['time_spent_on_the_page']\n",
        "time_spanish = new_page[new_page['language_preferred']=='Spanish']['time_spent_on_the_page']"
      ],
      "metadata": {
        "id": "n4WFL-u3XPRT"
      },
      "id": "n4WFL-u3XPRT",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 5: Calculate the p-value"
      ],
      "metadata": {
        "id": "KXkF459PXwDV"
      },
      "id": "KXkF459PXwDV"
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the required function\n",
        "from scipy.stats import f_oneway\n",
        "\n",
        "# Find the p-value\n",
        "test_stat, p_value = f_oneway(time_english,time_french,time_spanish)\n",
        "print('The p-value is ', p_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hNUCBJS3X3K4",
        "outputId": "aa612720-ab12-49f7-c50d-20c76c4a3a90"
      },
      "id": "hNUCBJS3X3K4",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The p-value is  0.43204138694325955\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 6: Compare the p-value with alpha ($\\alpha$)"
      ],
      "metadata": {
        "id": "jnK1i0fFYRV9"
      },
      "id": "jnK1i0fFYRV9"
    },
    {
      "cell_type": "code",
      "source": [
        "# Printing the conclusion based on p-value\n",
        "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.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xQMW2cxaYZb7",
        "outputId": "f8f86984-9562-4cd5-dd39-5e44b0db593e"
      },
      "id": "xQMW2cxaYZb7",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "As the p-value 0.43204138694325955 is greater than the level of significance, we fail to reject the null hypothesis.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 7: Draw inference"
      ],
      "metadata": {
        "id": "ZuEP1Z_AZBEA"
      },
      "id": "ZuEP1Z_AZBEA"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since the p-value is greater than the level of significance (0.05), we fail to reject the null hypothesis. Hence, there isn't enough statistical evidence to say that the mean time spent on the new page with respect to the language preferred is the same."
      ],
      "metadata": {
        "id": "ma_ek2wPZJuA"
      },
      "id": "ma_ek2wPZJuA"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3acbc947"
      },
      "source": [
        "## Conclusion and Business Recommendations"
      ],
      "id": "3acbc947"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Conclusions"
      ],
      "metadata": {
        "id": "sziGhudWEZu0"
      },
      "id": "sziGhudWEZu0"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21fb1063"
      },
      "source": [
        "The data consists of 100 users of the E-news Express website. It is split evenly between 2 groups: Control group was served the old landing page, and Treatment group was served the new landing page.\n",
        "* On average users spend more time on the new page (about 6 minutes) than on the old page (around 4 minutes). Showing more success on the new page than the old.\n",
        "* Users in the new page spend between 3 and 9 minutes with a few outliers on both ends. The time in the new page is higher than the time spent in the old page (between less than 1 and 10 minutes).\n",
        "* The maximum amount of time spent on the website is 10.71 minutes and a minumum of 0.19 minutes with an average of 5.38. These numbers seem low for the e-news platform.\n",
        "* 54% of the users subscribed to the website which also seems low. The new landing page showed that users a more likely to convert.\n",
        "* A total of 3 languages (English, French, and Spanish) were analyzed in the data. All 3 languages have a similar amount of users and average time spent on the page. However, it seems like Spanish users spend more time on the page than the others. Further analysis was done.\n",
        "* At first glance English users seem more likely to convert to subscribers and further analysis was also done to check this."
      ],
      "id": "21fb1063"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Recommendations"
      ],
      "metadata": {
        "id": "EcIVmwtNEb9V"
      },
      "id": "EcIVmwtNEb9V"
    },
    {
      "cell_type": "markdown",
      "source": [
        "* As demonstrated on the t-test and proportions z-test. The news portal should fully implement the new landing page as it shows a more successful conversion rate and more time spent on the page.\n",
        "* The company should continue to provide news on the 3 languages analyzed (English, French, and Spanish) and perhaps expand on other popular languages.\n",
        "* The chi-square test of independence showed that there isn't enough statistical evidence to say that the converted status is dependent of language preferred. Therefore, the company should apply their resources into the new page since it has a higher conversion rate.\n",
        "* At the same time, the average time spent on the page is similar regardless of the language preferred by the user. Once again, the time spent on the page is higher on the new than the old and doesn't seem to be related to the language preferred.\n",
        "* Converted users spend more time on the page than not converted. In order to increase time spent on the website, the company should seek more subscribers by using the new page.\n",
        "* The company could allow subscribed users to save and share articles.\n",
        "* By providing a free subscription, the company could incite users to subscribe and limit the amount of articles that can be read without a subscription.\n",
        "* Further data gathering and analysis can be done to determine what types of news users look for and read."
      ],
      "metadata": {
        "id": "-RGlM3ZxI1C6"
      },
      "id": "-RGlM3ZxI1C6"
    },
    {
      "cell_type": "markdown",
      "source": [
        "___"
      ],
      "metadata": {
        "id": "U0dAd4i0iCHo"
      },
      "id": "U0dAd4i0iCHo"
    }
  ]
}