Project_Business_Statistics_github.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6yDxo9u0d9Na",
   "metadata": {
    "id": "6yDxo9u0d9Na"
   },
   "source": [
    "# Project Business Statistics: E-news Express"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f3ba03a",
   "metadata": {
    "id": "0f3ba03a"
   },
   "source": [
    "## Context\n",
    "\n",
    "An online news portal aims to expand its business by acquiring new subscribers. Every visitor to the website takes certain actions based on their interest. The company plans to analyze these interests and wants to determine whether a new feature will be effective or not. Companies often analyze users' responses to two variants of a product to decide which of the two variants is more effective. This experimental technique is known as a/b testing that is used to determine whether a new feature attracts users based on a chosen metric.\n",
    "\n",
    "## Objective\n",
    "Suppose you are hired as a Data Scientist in E-news Express. The design team of the company has created a new landing page. You have been assigned the task to decide whether the new landing page is more effective to gather new subscribers. Suppose you randomly selected 100 users and divided them equally into two groups. The old landing page is served to the first group (control group) and the new landing page is served to the second group (treatment group). Various data about the customers in both groups are collected in 'abtest.csv'. Perform the statistical analysis to answer the following questions using the collected data.\n",
    "\n",
    "1. Explore the dataset and extract insights using Exploratory Data Analysis.\n",
    "\n",
    "2. Do the users spend more time on the new landing page than the existing landing page?\n",
    "\n",
    "3. 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",
    "4. Does the converted status depend on the preferred language? [Hint: Create a contingency table using the pandas.crosstab() function]\n",
    "\n",
    "5. Is the time spent on the new page same for the different language users?\n",
    "\n",
    "\n",
    "*Consider a significance level of 0.05 for all tests.\n",
    "\n",
    "The  idea  behind  answering  these  questions  is  to  decide  whether  the  new  page  is  effective  enough  to  gather  new subscribers for the news portal. We will perform the statistical analysis on the collected data to make the business decision.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c2d35fa",
   "metadata": {
    "id": "0c2d35fa"
   },
   "source": [
    "## Data Dictionary\n",
    "\n",
    "1. user_id - This represents the user ID of the person visiting the website.\n",
    "\n",
    "2. group - This represents whether the user belongs to the first group (control) or the second group (treatment).\n",
    "\n",
    "3. landing_page - This represents whether the landing page is new or old.\n",
    "\n",
    "4. time_spent_on_the_page - This represents the time (in minutes) spent by the user on the landing page.\n",
    "\n",
    "5. converted - This represents whether the user gets converted to a subscriber of the news portal or not.\n",
    "\n",
    "6. language_preferred - This represents the language chosen by the user to view the landing page."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6a3d218",
   "metadata": {
    "id": "a6a3d218"
   },
   "source": [
    "## Import all the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "658c5dec",
   "metadata": {
    "id": "658c5dec"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27201534",
   "metadata": {
    "id": "27201534"
   },
   "source": [
    "### Exploratory Data Analysis - Step by step approach\n",
    "\n",
    "Typical Data exploration activity consists of the following steps:\n",
    "1.\tImporting Data\n",
    "2.\tVariable Identification\n",
    "3.  Variable Transformation/Feature Creation\n",
    "4.  Missing value detection\n",
    "5.\tUnivariate Analysis\n",
    "6.\tBivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6970dd99",
   "metadata": {
    "id": "6970dd99"
   },
   "source": [
    "### Reading the Data into a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d3b95640",
   "metadata": {
    "id": "d3b95640"
   },
   "outputs": [],
   "source": [
    "# complete the code below to load the dataset\n",
    "df = pd.read_csv('abtest.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4e471fd",
   "metadata": {
    "id": "a4e471fd"
   },
   "source": [
    "### Data Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "e1ad11d4",
   "metadata": {
    "id": "e1ad11d4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>time_spent_on_the_page</th>\n",
       "      <th>converted</th>\n",
       "      <th>language_preferred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>546592</td>\n",
       "      <td>control</td>\n",
       "      <td>old</td>\n",
       "      <td>3.48</td>\n",
       "      <td>no</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>546468</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new</td>\n",
       "      <td>7.13</td>\n",
       "      <td>yes</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>546462</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new</td>\n",
       "      <td>4.40</td>\n",
       "      <td>no</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>546567</td>\n",
       "      <td>control</td>\n",
       "      <td>old</td>\n",
       "      <td>3.02</td>\n",
       "      <td>no</td>\n",
       "      <td>French</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>546459</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new</td>\n",
       "      <td>4.75</td>\n",
       "      <td>yes</td>\n",
       "      <td>Spanish</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id      group landing_page  time_spent_on_the_page converted  \\\n",
       "0   546592    control          old                    3.48        no   \n",
       "1   546468  treatment          new                    7.13       yes   \n",
       "2   546462  treatment          new                    4.40        no   \n",
       "3   546567    control          old                    3.02        no   \n",
       "4   546459  treatment          new                    4.75       yes   \n",
       "\n",
       "  language_preferred  \n",
       "0            Spanish  \n",
       "1            English  \n",
       "2            Spanish  \n",
       "3             French  \n",
       "4            Spanish  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view a few rows of the data frame\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "6n1XyRa3_VMg",
   "metadata": {
    "id": "6n1XyRa3_VMg"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 6)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view the shape of the data frame\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "BeDOZQ99_mQE",
   "metadata": {
    "id": "BeDOZQ99_mQE"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 6 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   user_id                 100 non-null    int64  \n",
      " 1   group                   100 non-null    object \n",
      " 2   landing_page            100 non-null    object \n",
      " 3   time_spent_on_the_page  100 non-null    float64\n",
      " 4   converted               100 non-null    object \n",
      " 5   language_preferred      100 non-null    object \n",
      "dtypes: float64(1), int64(1), object(4)\n",
      "memory usage: 4.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# check the data types of the columns in the data frame\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8cc04df",
   "metadata": {},
   "source": [
    "$Observation$ there is no none values. 100 row and 6 columms. time spend in the page is float64  while other columns are objec"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "UqA39o_2_7-j",
   "metadata": {
    "id": "UqA39o_2_7-j"
   },
   "source": [
    "### Fixing the data types"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "yr0H05A9ABMK",
   "metadata": {
    "id": "yr0H05A9ABMK"
   },
   "source": [
    "`converting \"objects\" to \"category\" reduces the space required to store the DataFrame. It also helps in analysis`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "DgTMQzFYADAs",
   "metadata": {
    "id": "DgTMQzFYADAs"
   },
   "outputs": [],
   "source": [
    "df['group'] = df['group'].astype('category')\n",
    "df['landing_page'] = df['landing_page'].astype('category')\n",
    "df['converted'] = df['converted'].astype('category')\n",
    "df['language_preferred'] = df['language_preferred'].astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "UdbuWJTvukrz",
   "metadata": {
    "id": "UdbuWJTvukrz"
   },
   "source": [
    "### Check for missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "NPQfOZgAALLC",
   "metadata": {
    "id": "NPQfOZgAALLC"
   },
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.isna().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b0d2889",
   "metadata": {},
   "source": [
    "$observation$ there is no nan values "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eg5bcF7Qd8S",
   "metadata": {
    "id": "9eg5bcF7Qd8S"
   },
   "source": [
    "### Five Point Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "mHcaTfF0Abjp",
   "metadata": {
    "id": "mHcaTfF0Abjp"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>100.000000</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>NaN</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>NaN</td>\n",
       "      <td>treatment</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>NaN</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>546517.000000</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>52.295779</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.378166</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>546443.000000</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>546467.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.880000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>546492.500000</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>546567.250000</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>546592.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.710000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              user_id      group landing_page  time_spent_on_the_page  \\\n",
       "count      100.000000        100          100              100.000000   \n",
       "unique            NaN          2            2                     NaN   \n",
       "top               NaN  treatment          old                     NaN   \n",
       "freq              NaN         50           50                     NaN   \n",
       "mean    546517.000000        NaN          NaN                5.377800   \n",
       "std         52.295779        NaN          NaN                2.378166   \n",
       "min     546443.000000        NaN          NaN                0.190000   \n",
       "25%     546467.750000        NaN          NaN                3.880000   \n",
       "50%     546492.500000        NaN          NaN                5.415000   \n",
       "75%     546567.250000        NaN          NaN                7.022500   \n",
       "max     546592.000000        NaN          NaN               10.710000   \n",
       "\n",
       "       converted language_preferred  \n",
       "count        100                100  \n",
       "unique         2                  3  \n",
       "top          yes            Spanish  \n",
       "freq          54                 34  \n",
       "mean         NaN                NaN  \n",
       "std          NaN                NaN  \n",
       "min          NaN                NaN  \n",
       "25%          NaN                NaN  \n",
       "50%          NaN                NaN  \n",
       "75%          NaN                NaN  \n",
       "max          NaN                NaN  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# write your code here to print the summary statistics\n",
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "-EckQbpMAsp1",
   "metadata": {
    "id": "-EckQbpMAsp1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "treatment    50\n",
       "control      50\n",
       "Name: group, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['group'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6LlJXjzBA_jP",
   "metadata": {
    "id": "6LlJXjzBA_jP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "old    50\n",
       "new    50\n",
       "Name: landing_page, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['landing_page'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "z80t7LK5BCBT",
   "metadata": {
    "id": "z80t7LK5BCBT"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yes    54\n",
       "no     46\n",
       "Name: converted, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['converted'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "_g2CBicdBDF0",
   "metadata": {
    "id": "_g2CBicdBDF0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Spanish    34\n",
       "French     34\n",
       "English    32\n",
       "Name: language_preferred, dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['language_preferred'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "CIljftw7BEnX",
   "metadata": {
    "id": "CIljftw7BEnX"
   },
   "source": [
    "**Observations**\n",
    "* There are 100 unique users.\n",
    "* There are 2 unique groups - control and treatment. Each group consists of 50 users.\n",
    "* There are 2 landing_pages - new and old.\n",
    "* Overall, 54 users get converted and 46 users do not get converted after visiting the landing page.\n",
    "* There are 3 unique preferred languages - English, French, and Spanish."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68f3b2c9",
   "metadata": {
    "id": "68f3b2c9"
   },
   "source": [
    "### Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1932d0f3",
   "metadata": {
    "id": "1932d0f3"
   },
   "outputs": [],
   "source": [
    "# function to plot a boxplot and a histogram along the same scale.\n",
    "\n",
    "\n",
    "def histogram_boxplot(data, feature, figsize=(12, 7), kde=False, bins=None):\n",
    "    \"\"\"\n",
    "    Boxplot and histogram combined\n",
    "\n",
    "    data: dataframe\n",
    "    feature: dataframe column\n",
    "    figsize: size of figure (default (12,7))\n",
    "    kde: whether to show the density curve (default False)\n",
    "    bins: number of bins for histogram (default None)\n",
    "    \"\"\"\n",
    "    f2, (ax_box2, ax_hist2) = plt.subplots(\n",
    "        nrows=2,  # Number of rows of the subplot grid= 2\n",
    "        sharex=True,  # x-axis will be shared among all subplots\n",
    "        gridspec_kw={\"height_ratios\": (0.25, 0.75)},\n",
    "        figsize=figsize,\n",
    "    )  # creating the 2 subplots\n",
    "    sns.boxplot(\n",
    "        data=data, x=feature, ax=ax_box2, showmeans=True, color=\"violet\"\n",
    "    )  # boxplot will be created and a star will indicate the mean value of the column\n",
    "    sns.histplot(\n",
    "        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette=\"winter\"\n",
    "    ) if bins else sns.histplot(\n",
    "        data=data, x=feature, kde=kde, ax=ax_hist2\n",
    "    )  # For histogram\n",
    "    ax_hist2.axvline(\n",
    "        data[feature].mean(), color=\"green\", linestyle=\"--\"\n",
    "    )  # Add mean to the histogram\n",
    "    ax_hist2.axvline(\n",
    "        data[feature].median(), color=\"black\", linestyle=\"-\"\n",
    "    )  # Add median to the histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "NAkPh1ThBTGD",
   "metadata": {
    "id": "NAkPh1ThBTGD"
   },
   "source": [
    "#### Time spent on the page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "3dsXz9R6BXIX",
   "metadata": {
    "id": "3dsXz9R6BXIX"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram_boxplot(df,\"time_spent_on_the_page\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2fce6350",
   "metadata": {
    "id": "2fce6350"
   },
   "outputs": [],
   "source": [
    "# function to create labeled barplots\n",
    "\n",
    "\n",
    "def labeled_barplot(data, feature, perc=False, n=None):\n",
    "    \"\"\"\n",
    "    Barplot with percentage at the top\n",
    "\n",
    "    data: dataframe\n",
    "    feature: dataframe column\n",
    "    perc: whether to display percentages instead of count (default is False)\n",
    "    n: displays the top n category levels (default is None, i.e., display all levels)\n",
    "    \"\"\"\n",
    "\n",
    "    total = len(data[feature])  # length of the column\n",
    "    count = data[feature].nunique()\n",
    "    if n is None:\n",
    "        plt.figure(figsize=(count + 1, 5))\n",
    "    else:\n",
    "        plt.figure(figsize=(n + 1, 5))\n",
    "\n",
    "    plt.xticks(rotation=90, fontsize=15)\n",
    "    ax = sns.countplot(data=data, x=feature, palette=\"Paired\", order=data[feature].value_counts().index[:n].sort_values())\n",
    "\n",
    "    for p in ax.patches:\n",
    "        if perc == True:\n",
    "            label = \"{:.1f}%\".format(100 * p.get_height() / total)  # percentage of each class of the category\n",
    "        else:\n",
    "            label = p.get_height()  # count of each level of the category\n",
    "\n",
    "        x = p.get_x() + p.get_width() / 2  # width of the plot\n",
    "        y = p.get_height()      # height of the plot\n",
    "\n",
    "        ax.annotate(label, (x, y), ha=\"center\", va=\"center\", size=12, xytext=(0, 5), textcoords=\"offset points\")  # annotate the percentage\n",
    "\n",
    "    plt.show()  # show the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6f99481",
   "metadata": {
    "id": "e6f99481"
   },
   "source": [
    "#### Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ROvZjuEGBm0t",
   "metadata": {
    "id": "ROvZjuEGBm0t"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labeled_barplot(df,'group',perc = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sSdc8YDbBr9Y",
   "metadata": {
    "id": "sSdc8YDbBr9Y"
   },
   "source": [
    "#### Landing page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "CbIZgbOzBuxF",
   "metadata": {
    "id": "CbIZgbOzBuxF"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "labeled_barplot(df,'landing_page',perc = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3Rex1rUxB3YZ",
   "metadata": {
    "id": "3Rex1rUxB3YZ"
   },
   "source": [
    "#### Converted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "qIMrpxF4B7an",
   "metadata": {
    "id": "qIMrpxF4B7an"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 216x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# write the code to plot the barplot of 'converted' column\n",
    "\n",
    "labeled_barplot(df,'converted',perc = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "agkGFqokCOb8",
   "metadata": {
    "id": "agkGFqokCOb8"
   },
   "source": [
    "#### Language preferred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "BYnVZH8ECQaY",
   "metadata": {
    "id": "BYnVZH8ECQaY"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 288x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "labeled_barplot(df,'language_preferred',perc = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fad2de2f",
   "metadata": {
    "id": "fad2de2f"
   },
   "source": [
    "### Bivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58e9a1d0",
   "metadata": {
    "id": "58e9a1d0"
   },
   "source": [
    "#### Landing page vs Time spent on the page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "2y7N8qQiDEfn",
   "metadata": {
    "id": "2y7N8qQiDEfn"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 9))\n",
    "sns.histplot(data = df, x = 'time_spent_on_the_page', hue = 'landing_page',kde=True)\n",
    "plt.show()\n",
    "#time spend on the page mean are higher for new landing page  than old landing page "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c2b09b0",
   "metadata": {},
   "source": [
    "$observation$: the mean time spend on the page are higher for new landing page  than old landing page"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "q9dW3eB-DTBF",
   "metadata": {
    "id": "q9dW3eB-DTBF"
   },
   "source": [
    "#### Conversion status vs Time spent on the page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "yPqV_UsvDURc",
   "metadata": {
    "id": "yPqV_UsvDURc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 9))\n",
    "sns.histplot(data = df, x = 'time_spent_on_the_page', hue = 'converted',kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d0b0f84",
   "metadata": {},
   "source": [
    "$observation$: the mean time spend on the page are higher for converted page  than none converted page"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67Ozc0p8Dmbe",
   "metadata": {
    "id": "67Ozc0p8Dmbe"
   },
   "source": [
    "#### Language preferred vs Time spent on the page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "IaFp53rDDn2W",
   "metadata": {
    "id": "IaFp53rDDn2W"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(9, 9))\n",
    "sn=sns.boxplot(data = df, x = 'time_spent_on_the_page', y = 'language_preferred',showmeans=True)\n",
    "sn.set_alpha=50\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5fdf0c8",
   "metadata": {
    "id": "e5fdf0c8"
   },
   "source": [
    "### Perform Visual Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6eZJa41eg21n",
   "metadata": {
    "id": "6eZJa41eg21n"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visual analysis of the time spent on the new page\n",
    "# and the time spent on the old page\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.violinplot(x = 'landing_page', y = 'time_spent_on_the_page', data = df,showmeans=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58571dc5",
   "metadata": {},
   "source": [
    "$observation$: the median time spend on the page are higher for new landing page  than old landing page"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56b3dafd",
   "metadata": {
    "id": "56b3dafd"
   },
   "source": [
    "### Step 1: Define the null and alternate hypotheses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2ac77ef",
   "metadata": {
    "id": "d2ac77ef"
   },
   "source": [
    "H0:u_new=u_old   \n",
    "H1:u_new>n_old\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7ee4907",
   "metadata": {
    "id": "c7ee4907"
   },
   "source": [
    "### Step 2: Select Appropriate test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c183cd7",
   "metadata": {
    "id": "2c183cd7"
   },
   "source": [
    "This is a one-tailed test concerning two population means from two independent populations. The population standard deviations are unknown. **Based on this information, select the appropriate test**. \n",
    "* I select t_test "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f58c9a7",
   "metadata": {
    "id": "3f58c9a7"
   },
   "source": [
    "### Step 3: Decide the significance level"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d7d7ee",
   "metadata": {
    "id": "92d7d7ee"
   },
   "source": [
    "As given in the problem statement, we select α = 0.05."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fed2eac",
   "metadata": {
    "id": "5fed2eac"
   },
   "source": [
    "### Step 4: Collect and prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "f5a59495",
   "metadata": {
    "id": "f5a59495"
   },
   "outputs": [],
   "source": [
    "# create subsetted data frame for new landing page users \n",
    "time_spent_new = df[df['landing_page'] == 'new']['time_spent_on_the_page']\n",
    "# create subsetted data frame for old landing page users\n",
    "time_spent_old =df[df['landing_page'] == 'old']['time_spent_on_the_page']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "tNGXW88MFxFz",
   "metadata": {
    "id": "tNGXW88MFxFz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sample standard deviation of the time spent on the new page is: 1.82\n",
      "The sample standard deviation of the time spent on the new page is: 2.58\n"
     ]
    }
   ],
   "source": [
    "print('The sample standard deviation of the time spent on the new page is:', round(time_spent_new.std(),2))\n",
    "print('The sample standard deviation of the time spent on the new page is:', round(time_spent_old.std(),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Qa4jX7dwF1w-",
   "metadata": {
    "id": "Qa4jX7dwF1w-"
   },
   "source": [
    "**Based on the sample standard deviations of the two groups, decide whether the population standard deviations can be assumed to be equal or unequal**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5c5258d",
   "metadata": {
    "id": "f5c5258d"
   },
   "source": [
    "### Step 5: Calculate the p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "fac8594a",
   "metadata": {
    "id": "fac8594a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is 0.0001316123528095005\n"
     ]
    }
   ],
   "source": [
    "# complete the code to import the required function\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "# write the code to calculate the p-value\n",
    "test_stat, p_value = ttest_ind(time_spent_new,time_spent_old,alternative='greater')   #write your code here\n",
    "\n",
    "print('The p-value is', p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "359b12f8",
   "metadata": {
    "id": "359b12f8"
   },
   "source": [
    "### Step 6: Compare the p-value with $\\alpha$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "7be47289",
   "metadata": {
    "id": "7be47289"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As the p-value 0.0001316123528095005 is less than the level of significance, we reject the null hypothesis.\n"
     ]
    }
   ],
   "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.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9affc84",
   "metadata": {
    "id": "d9affc84"
   },
   "source": [
    "### Step 7:  Draw inference"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b16dd4b",
   "metadata": {
    "id": "6b16dd4b"
   },
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ZmZfv9znHmuI",
   "metadata": {
    "id": "ZmZfv9znHmuI"
   },
   "source": [
    "### Perform Visual Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "      ...  \n",
       "95    False\n",
       "96    False\n",
       "97    False\n",
       "98    False\n",
       "99    False\n",
       "Name: landing_page, Length: 100, dtype: bool"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['landing_page'].isin(['yes'])\n",
    "df['landing_page'].isin(['no'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "b7f52898",
   "metadata": {
    "id": "b7f52898"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# write the code to visually compare the conversion rate for the new page and the conversion rate for the old page\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.countplot(hue = 'converted', x = 'landing_page', data = df)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "cf30a2a3",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'matplotlib.pyplot' has no attribute 'bar_label'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\ryass.DESKTOP-0A1NS33\\OneDrive\\GreatLearning_DSBA\\business_analytics\\poject\\ENews_Express_Learner_Notebook%5BLow_Code_Version%5D.ipynb Cell 75'\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/ryass.DESKTOP-0A1NS33/OneDrive/GreatLearning_DSBA/business_analytics/poject/ENews_Express_Learner_Notebook%255BLow_Code_Version%255D.ipynb#ch0000133?line=1'>2</a>\u001b[0m p11\u001b[39m=\u001b[39mp1\u001b[39m.\u001b[39mplot(kind\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mbar\u001b[39m\u001b[39m'\u001b[39m,  figsize\u001b[39m=\u001b[39m(\u001b[39m8\u001b[39m, \u001b[39m8\u001b[39m), rot\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, ylabel\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mPercentage \u001b[39m\u001b[39m%\u001b[39m\u001b[39m'\u001b[39m, title\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/ryass.DESKTOP-0A1NS33/OneDrive/GreatLearning_DSBA/business_analytics/poject/ENews_Express_Learner_Notebook%255BLow_Code_Version%255D.ipynb#ch0000133?line=4'>5</a>\u001b[0m \u001b[39mfor\u001b[39;00m p \u001b[39min\u001b[39;00m p11\u001b[39m.\u001b[39mcontainers:\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/ryass.DESKTOP-0A1NS33/OneDrive/GreatLearning_DSBA/business_analytics/poject/ENews_Express_Learner_Notebook%255BLow_Code_Version%255D.ipynb#ch0000133?line=5'>6</a>\u001b[0m     plt\u001b[39m.\u001b[39;49mbar_label(p, fmt\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m%.1f\u001b[39;00m\u001b[39m%%\u001b[39;00m\u001b[39m'\u001b[39m, label_type\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39medge\u001b[39m\u001b[39m'\u001b[39m)\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'bar_label'"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1=pd.crosstab(  df['landing_page'],df['converted'],margins=False,margins_name=\"Total\",normalize='index').round(4)*100\n",
    "p11=p1.plot(kind='bar',  figsize=(8, 8), rot=0, ylabel='Percentage %', title=\"\")\n",
    "\n",
    "\n",
    "for p in p11.containers:\n",
    "    plt.bar_label(p, fmt='%.1f%%', label_type='edge')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0LKutu87H7yi",
   "metadata": {
    "id": "0LKutu87H7yi"
   },
   "source": [
    "### Step 1: Define the null and alternate hypotheses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "-l--nMMNH7yx",
   "metadata": {
    "id": "-l--nMMNH7yx"
   },
   "source": [
    "H0:Pn=Po\n",
    "H1:Pn>Po\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3QVRe46rH7yx",
   "metadata": {
    "id": "3QVRe46rH7yx"
   },
   "source": [
    "### Step 2: Select Appropriate test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "VAlgwuRXIA8Z",
   "metadata": {
    "id": "VAlgwuRXIA8Z"
   },
   "source": [
    "This is a one-tailed test concerning two population proportions from two independent populations. **Based on this information, select the appropriate test**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "QFo6RyfxIM9j",
   "metadata": {
    "id": "QFo6RyfxIM9j"
   },
   "source": [
    "### Step 3: Decide the significance level"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "NLuVA-hpIM9k",
   "metadata": {
    "id": "NLuVA-hpIM9k"
   },
   "source": [
    "As given in the problem statement, we select α = 0.05."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "L6XEIeGgIM9m",
   "metadata": {
    "id": "L6XEIeGgIM9m"
   },
   "source": [
    "### Step 4: Collect and prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "Sbtfr2N6INtG",
   "metadata": {
    "id": "Sbtfr2N6INtG"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The numbers of converted users in treatment group and numbers of converted users in control  group are 33 and 21 respectively\n",
      "The numbers of users served the new and old pages are 50 and 50 respectively\n"
     ]
    }
   ],
   "source": [
    "# calculate the number of converted users in the treatment group\n",
    "new_converted = df[df['group'] == 'treatment']['converted'].value_counts()['yes']\n",
    "# calculate the number of converted users in the control group\n",
    "old_converted = df[df['group'] == 'control']['converted'].value_counts()['yes']\n",
    "print('The numbers of converted users in treatment group and numbers of converted users in control  group are {0} and {1} respectively'.format(new_converted, old_converted ))\n",
    "n_control = df.group.value_counts()['control'] # total number of users in the control group\n",
    "n_treatment = df.group.value_counts()['treatment'] # total number of users in the treatment group\n",
    "print('The numbers of users served the new and old pages are {0} and {1} respectively'.format(n_control, n_treatment ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Szx7nkG-I-87",
   "metadata": {
    "id": "Szx7nkG-I-87"
   },
   "source": [
    "### Step 5: Calculate the p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "0kAItTuKI-88",
   "metadata": {
    "id": "0kAItTuKI-88"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is 0.008026308204056278\n"
     ]
    }
   ],
   "source": [
    "# complete the code to import the required function\n",
    "from statsmodels.stats.proportion import proportions_ztest  \n",
    "\n",
    "# write the code to calculate the p-value\n",
    "test_stat, p_value = proportions_ztest([new_converted,old_converted],[n_control,n_treatment],alternative='larger')   # write your code here\n",
    "\n",
    "print('The p-value is', p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3koFwBllI-88",
   "metadata": {
    "id": "3koFwBllI-88"
   },
   "source": [
    "### Step 6: Compare the p-value with $\\alpha$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "OTR1ANvmI-88",
   "metadata": {
    "id": "OTR1ANvmI-88"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As the p-value 0.008026308204056278 is less than the level of significance, we reject the null hypothesis.\n"
     ]
    }
   ],
   "source": [
    "# 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.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "LzcK3E4rI-89",
   "metadata": {
    "id": "LzcK3E4rI-89"
   },
   "source": [
    "### Step 7:  Draw inference"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "u_KsjYc_I-89",
   "metadata": {
    "id": "u_KsjYc_I-89"
   },
   "source": [
    "conversion rate visiting the new pages is higher to conversion rate of visiting the pages \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0Yx13sMQJUHR",
   "metadata": {
    "id": "0Yx13sMQJUHR"
   },
   "source": [
    "### Perform Visual Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "1l_ue1yEJUHf",
   "metadata": {
    "id": "1l_ue1yEJUHf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2749c016730>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# write the code to visually plot the dependency between conversion status and preferred langauge\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.countplot(x = 'converted', hue = 'language_preferred', data = df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pG7oKB7QJUHf",
   "metadata": {
    "id": "pG7oKB7QJUHf"
   },
   "source": [
    "### Step 1: Define the null and alternate hypotheses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0oPUn7OtJUHg",
   "metadata": {
    "id": "0oPUn7OtJUHg"
   },
   "source": [
    "$H0:$ Converted statue is independent of preferred languages \n",
    "\n",
    "$H1:$ Converted statue is not independent of preferred languages \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "TQTrplAaJUHg",
   "metadata": {
    "id": "TQTrplAaJUHg"
   },
   "source": [
    "### Step 2: Select Appropriate test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fLW6QhhPJUHg",
   "metadata": {
    "id": "fLW6QhhPJUHg"
   },
   "source": [
    "This is a problem of the test of independence, concerning two categorical variables - converted status and preferred language. **Based on this information, select the appropriate test.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hOZSCAvrJUHg",
   "metadata": {
    "id": "hOZSCAvrJUHg"
   },
   "source": [
    "### Step 3: Decide the significance level"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "wDGvFYjRJUHg",
   "metadata": {
    "id": "wDGvFYjRJUHg"
   },
   "source": [
    "As given in the problem statement, we select α = 0.05."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rRZiuBfOJUHg",
   "metadata": {
    "id": "rRZiuBfOJUHg"
   },
   "source": [
    "### Step 4: Collect and prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "zzvjITNZJUHh",
   "metadata": {
    "id": "zzvjITNZJUHh"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2749c0f9ca0>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# complete the code to create a contingency table showing the distribution of the two categorical variables\n",
    "\n",
    "\n",
    "contingency_table = pd.crosstab(df['converted'],df['language_preferred'],normalize='index').plot.bar(stacked=True)  \n",
    "\n",
    "contingency_table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "WKsL1bnGJUHh",
   "metadata": {
    "id": "WKsL1bnGJUHh"
   },
   "source": [
    "### Step 5: Calculate the p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "5T87wmlEJUHh",
   "metadata": {
    "id": "5T87wmlEJUHh"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is 0.21298887487543447\n"
     ]
    }
   ],
   "source": [
    "# complete the code to import the required function\n",
    "from scipy.stats import chi2_contingency \n",
    "\n",
    "# write the code to calculate the p-value\n",
    "chi2, p_value, dof, exp_freq =  chi2_contingency(pd.crosstab(df['converted'],df['language_preferred']))  # write your code here\n",
    "\n",
    "print('The p-value is', p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "O7hxW9bhJUHh",
   "metadata": {
    "id": "O7hxW9bhJUHh"
   },
   "source": [
    "### Step 6: Compare the p-value with $\\alpha$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "z4i5F7TTJUHh",
   "metadata": {
    "id": "z4i5F7TTJUHh"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As the p-value 0.21298887487543447 is greater than the level of significance, we fail to reject the null hypothesis.\n"
     ]
    }
   ],
   "source": [
    "# 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.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "NpQs87d3JUHh",
   "metadata": {
    "id": "NpQs87d3JUHh"
   },
   "source": [
    "### Step 7:  Draw inference"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nWL_M80mJUHi",
   "metadata": {
    "id": "nWL_M80mJUHi"
   },
   "source": [
    "converted statues are independent of languages.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Zv5zTG0_KmOM",
   "metadata": {
    "id": "Zv5zTG0_KmOM"
   },
   "source": [
    "### Perform Visual Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "c8f1e4d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new DataFrame for users who got served the new page\n",
    "df_new = df[df['landing_page'] == 'new']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "juWwwrwzKmOa",
   "metadata": {
    "id": "juWwwrwzKmOa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'time spend on the new page')"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# write the code to visually plot the time spent on the new page for different language users\n",
    "\n",
    "p=sns.boxplot(data=df_new,x='language_preferred',y='time_spent_on_the_page',showmeans=True)\n",
    "p.set_ylabel('time spend on the new page')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "9vqA04lsK_aZ",
   "metadata": {
    "id": "9vqA04lsK_aZ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "language_preferred\n",
       "English    6.663750\n",
       "French     6.196471\n",
       "Spanish    5.835294\n",
       "Name: time_spent_on_the_page, dtype: float64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# write the code to calculate the mean time spent on the new page for different language users\n",
    "df_new.groupby('language_preferred').mean()['time_spent_on_the_page']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "BGDAeivqKmOa",
   "metadata": {
    "id": "BGDAeivqKmOa"
   },
   "source": [
    "### Step 1: Define the null and alternate hypotheses"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "B6_bJP8PKmOb",
   "metadata": {
    "id": "B6_bJP8PKmOb"
   },
   "source": [
    "$H0:$ U_en=U_sp=U_fr\n",
    "\n",
    "$H1:$ u are not equal\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "GNjvZTS2KmOb",
   "metadata": {
    "id": "GNjvZTS2KmOb"
   },
   "source": [
    "### Step 2: Select Appropriate test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lNNnMnJZKmOb",
   "metadata": {
    "id": "lNNnMnJZKmOb"
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   "source": [
    "This is a problem, concerning three population means. **Based on this information, select the appropriate test to compare the three population means.** Also, check the assumptions of normality and equality of variance for the three groups\n",
    "\n",
    "* For testing of normality, Shapiro-Wilk’s test is applied to the response variable.\n",
    "\n",
    "* For equality of variance, Levene test is applied to the response variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "p6vHcFUELmcg",
   "metadata": {
    "id": "p6vHcFUELmcg"
   },
   "source": [
    "### Shapiro-Wilk’s test\n",
    "\n",
    "We will test the null hypothesis\n",
    "\n",
    ">$H_0:$ Time spent on the new page follows a normal distribution\n",
    "\n",
    "against the alternative hypothesis\n",
    "\n",
    ">$H_a:$ Time spent on the new page does not follow a normal distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "umdl7KKSOD_H",
   "metadata": {
    "id": "umdl7KKSOD_H"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is 0.8040016293525696\n"
     ]
    }
   ],
   "source": [
    "# Assumption 1: Normality\n",
    "# import the required function\n",
    "from scipy.stats import shapiro\n",
    "\n",
    "# find the p-value\n",
    "w, p_value = shapiro(df_new['time_spent_on_the_page']) \n",
    "print('The p-value is', p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eKDD4G34OHl0",
   "metadata": {
    "id": "eKDD4G34OHl0"
   },
   "source": [
    "Since p-value of the test is very large than the 5% significance level, we fail to reject the null hypothesis that the response follows the normal distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "DJ2gMkg3OLCO",
   "metadata": {
    "id": "DJ2gMkg3OLCO"
   },
   "source": [
    "### Levene’s test\n",
    "\n",
    "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "ne-4PRCIOOcB",
   "metadata": {
    "id": "ne-4PRCIOOcB"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is 0.46711357711340173\n"
     ]
    }
   ],
   "source": [
    "#Assumption 2: Homogeneity of Variance\n",
    "#import the required function \n",
    "from scipy.stats import levene\n",
    "statistic, p_value = levene( df_new[df_new['language_preferred']==\"English\"]['time_spent_on_the_page'], \n",
    "                             df_new[df_new['language_preferred']==\"French\"]['time_spent_on_the_page'], \n",
    "                             df_new[df_new['language_preferred']==\"Spanish\"]['time_spent_on_the_page'])\n",
    "# find the p-value\n",
    "print('The p-value is', p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Eume1ARiORI4",
   "metadata": {
    "id": "Eume1ARiORI4"
   },
   "source": [
    "Since the p-value is large than the 5% significance level, we fail to reject the null hypothesis of homogeneity of variances."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "VTouvcdxKmOc",
   "metadata": {
    "id": "VTouvcdxKmOc"
   },
   "source": [
    "### Step 3: Decide the significance level"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cNtIWQCyKmOc",
   "metadata": {
    "id": "cNtIWQCyKmOc"
   },
   "source": [
    "As given in the problem statement, we select α = 0.05."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ni6wGl2MKmOc",
   "metadata": {
    "id": "ni6wGl2MKmOc"
   },
   "source": [
    "### Step 4: Collect and prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "XThNwfviKmOd",
   "metadata": {
    "id": "XThNwfviKmOd"
   },
   "outputs": [],
   "source": [
    "# create a subsetted data frame of the time spent on the new page by English language users \n",
    "time_spent_English = df_new[df_new['language_preferred']==\"English\"]['time_spent_on_the_page']\n",
    "# create subsetted data frames of the time spent on the new page by French and Spanish language users\n",
    "time_spent_French =    df_new[df_new['language_preferred']==\"French\"]['time_spent_on_the_page']# write your code here\n",
    "time_spent_Spanish =    df_new[df_new['language_preferred']==\"Spanish\"]['time_spent_on_the_page']# write your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8F-VCtK7KmOd",
   "metadata": {
    "id": "8F-VCtK7KmOd"
   },
   "source": [
    "### Step 5: Calculate the p-value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "D_jngM0gKmOd",
   "metadata": {
    "id": "D_jngM0gKmOd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The p-value is 0.43204138694325955\n"
     ]
    }
   ],
   "source": [
    "# complete the code to import the required function\n",
    "from scipy.stats import f_oneway\n",
    "\n",
    "# write the code to calculate the p-value\n",
    "test_stat, p_value =  f_oneway(time_spent_English,time_spent_French,time_spent_Spanish)  # write your code here\n",
    "\n",
    "print('The p-value is', p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6MvtzfvKmOe",
   "metadata": {
    "id": "a6MvtzfvKmOe"
   },
   "source": [
    "### Step 6: Compare the p-value with $\\alpha$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "Ep85AInOKmOe",
   "metadata": {
    "id": "Ep85AInOKmOe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "As the p-value 0.43204138694325955 is greater than the level of significance, we fail to reject the null hypothesis.\n"
     ]
    }
   ],
   "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.')"
   ]
  },
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   "attachments": {},
   "cell_type": "markdown",
   "id": "TNt8JyugKmOf",
   "metadata": {
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   },
   "source": [
    "Draw inference"
   ]
  }
 ],
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