MCurcio-AB-Testing.ipynb

{
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    "# 'catsVSdogs.com' A/B Testing Analysis\n",
    "\n",
    "To:&nbsp;&nbsp;&nbsp;&nbsp; Magnimind\n",
    "\n",
    "From: Matt Curcio, matt.curcio.us@gmail.com\n",
    "\n",
    "Date: 2023-07-01\n",
    "\n",
    "Re:&nbsp;&nbsp;&nbsp; 'catsVSdogs.com' AB Testing\n",
    "\n",
    "\n",
    "\n",
    "## Executive Summary\n",
    "\n",
    ">*Note: This report describes work for the mythical company, catsVSdogs.com.*\n",
    "\n",
    "Market research showed that using animal icons on the purchase page of www.catsVSdogs.com might promote a higher conversion rate, i.e. better sales. These cat and the dog icons were chosen for AB testing.\n",
    "\n",
    "<center><strong>Order Icon: Cat vs. Dog</strong></center>\n",
    "\n",
    "<center>\n",
    "    \n",
    "  | ![small-cat](figures/small-cat.jpeg) | ![small-dog](figures/small-dog.jpeg) |\n",
    "  |:-------:|:-------:|\n",
    "  | Control | Exposed | \n",
    "    \n",
    "</center>\n",
    "\n",
    "\n",
    "### Conclusions\n",
    " \n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    \n",
    "1.  [Fisher's Exact Test](#Fisher's-Exact-Test) (P-value = 0.531): There is no association between the two icons and any change in conversion rates.\n",
    "\n",
    "2. [Phi Coefficient](#Phi-Coefficient-of-Association) ($\\Phi$ = -0.0183): There is no relationship between the two icons and a change in conversion rates.\n",
    "\n",
    "3. [Z-Test](#Two-Sample-Z-test) (P-value = 0.518): There is no relationship between the two icons and a change in conversion rates.\n",
    "\n",
    "4. **Do not change the icons for www.catsVSdogs.com at this time.**\n",
    "</div>\n",
    "\n",
    "## Introduction\n",
    "\n",
    "This A/B Test was carried out between July 3-10, 2020, Friday to Friday with 1243 participants.\n",
    "\n",
    "<center>\n",
    "\n",
    "  | Experimental Data | Proportion |\n",
    "  |:------------------|-----------:|\n",
    "  | Control: Conversion-Ratio using Cat icon | 322 / 586 = 54.9% |\n",
    "  | Exposed: Conversion-Ratio using Dog icon | 349 / 657 = 53.1% |\n",
    "\n",
    "</center>\n",
    "\n",
    "### Three statistical tests:\n",
    "\n",
    "  1. [Z-test for Proportions](https://en.wikipedia.org/wiki/Z-test), \n",
    "\n",
    "  2. [Fisher's Exact Test](https://en.wikipedia.org/wiki/Fisher%27s_exact_test),\n",
    "\n",
    "  3. [Phi-Coefficient test](https://en.wikipedia.org/wiki/Phi_coefficient) for comparison.\n",
    "\n",
    "\n",
    "### Graphics\n",
    "\n",
    "1. [Barplot of dates](#Barplot-of-dates)\n",
    "\n",
    "2. [Histogram of Hours Vs Counts](#Histogram-of-Hours-Vs-Counts)\n",
    "\n",
    "3. [Browser Word Cloud](#Browser-Word-Cloud)\n",
    "\n",
    "4. [Device Make Word Cloud](#Device-Make-Word-Cloud)\n",
    "\n",
    "\n",
    "##  Initial Data Analysis\n",
    "\n",
    "Data can be found at: **ad-ab-testing**, https://www.kaggle.com/datasets/osuolaleemmanuel/ad-ab-testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e0cbdb09",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# conda install -c conda-forge wordcloud\n",
    "from wordcloud import WordCloud\n",
    "from scipy.stats import norm\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e44b95b7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dataframe dimensions:  8077 Observations & 9 features\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>auction_id</th>\n",
       "      <th>experiment</th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "      <th>device_make</th>\n",
       "      <th>platform_os</th>\n",
       "      <th>browser</th>\n",
       "      <th>yes</th>\n",
       "      <th>no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0008ef63-77a7-448b-bd1e-075f42c55e39</td>\n",
       "      <td>exposed</td>\n",
       "      <td>2020-07-10</td>\n",
       "      <td>8</td>\n",
       "      <td>Generic Smartphone</td>\n",
       "      <td>6</td>\n",
       "      <td>Chrome Mobile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>000eabc5-17ce-4137-8efe-44734d914446</td>\n",
       "      <td>exposed</td>\n",
       "      <td>2020-07-07</td>\n",
       "      <td>10</td>\n",
       "      <td>Generic Smartphone</td>\n",
       "      <td>6</td>\n",
       "      <td>Chrome Mobile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0016d14a-ae18-4a02-a204-6ba53b52f2ed</td>\n",
       "      <td>exposed</td>\n",
       "      <td>2020-07-05</td>\n",
       "      <td>2</td>\n",
       "      <td>E5823</td>\n",
       "      <td>6</td>\n",
       "      <td>Chrome Mobile WebView</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00187412-2932-4542-a8ef-3633901c98d9</td>\n",
       "      <td>control</td>\n",
       "      <td>2020-07-03</td>\n",
       "      <td>15</td>\n",
       "      <td>Samsung SM-A705FN</td>\n",
       "      <td>6</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>001a7785-d3fe-4e11-a344-c8735acacc2c</td>\n",
       "      <td>control</td>\n",
       "      <td>2020-07-03</td>\n",
       "      <td>15</td>\n",
       "      <td>Generic Smartphone</td>\n",
       "      <td>6</td>\n",
       "      <td>Chrome Mobile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             auction_id experiment        date  hour  \\\n",
       "0  0008ef63-77a7-448b-bd1e-075f42c55e39    exposed  2020-07-10     8   \n",
       "1  000eabc5-17ce-4137-8efe-44734d914446    exposed  2020-07-07    10   \n",
       "2  0016d14a-ae18-4a02-a204-6ba53b52f2ed    exposed  2020-07-05     2   \n",
       "3  00187412-2932-4542-a8ef-3633901c98d9    control  2020-07-03    15   \n",
       "4  001a7785-d3fe-4e11-a344-c8735acacc2c    control  2020-07-03    15   \n",
       "\n",
       "          device_make  platform_os                browser  yes  no  \n",
       "0  Generic Smartphone            6          Chrome Mobile    0   0  \n",
       "1  Generic Smartphone            6          Chrome Mobile    0   0  \n",
       "2               E5823            6  Chrome Mobile WebView    0   1  \n",
       "3   Samsung SM-A705FN            6               Facebook    0   0  \n",
       "4  Generic Smartphone            6          Chrome Mobile    0   0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'data/'\n",
    "filename = 'AdSmartABdata.csv'\n",
    "df = pd.read_csv(path+filename)\n",
    "\n",
    "print('\\nDataframe dimensions: ',\n",
    "      df.shape[0], 'Observations &', df.shape[1], 'features')\n",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3645cfd",
   "metadata": {},
   "source": [
    "### Note 1\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    \n",
    "1. The `zip` file has NO descriptive information on the columns `auction_id` and `platform_os` columns. These variables will not be used in this analysis.\n",
    "\n",
    "</dev>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ad09942d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "auction_id     0\n",
       "experiment     0\n",
       "date           0\n",
       "hour           0\n",
       "device_make    0\n",
       "platform_os    0\n",
       "browser        0\n",
       "yes            0\n",
       "no             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for NULLS: NO NULLS FOUND\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c930354e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8077 entries, 0 to 8076\n",
      "Data columns (total 7 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   experiment   8077 non-null   object\n",
      " 1   date         8077 non-null   object\n",
      " 2   hour         8077 non-null   int64 \n",
      " 3   device_make  8077 non-null   object\n",
      " 4   browser      8077 non-null   object\n",
      " 5   yes          8077 non-null   int64 \n",
      " 6   no           8077 non-null   int64 \n",
      "dtypes: int64(3), object(4)\n",
      "memory usage: 441.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Reduce all letters to lower case\n",
    "\n",
    "df['device_make'] = df['device_make'].str.lower()\n",
    "df['browser'] = df['browser'].str.lower()\n",
    "df['experiment'] = df['experiment'].str.lower()\n",
    "\n",
    "# Delete columns 'auction_id'(0) & 'platform_os'(5) \n",
    "\n",
    "df_mod = df.drop(df.columns[[0, 5]], axis=1, inplace=False)\n",
    "df_mod.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d5087d8",
   "metadata": {
    "scrolled": true
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   "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>experiment</th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "      <th>device_make</th>\n",
       "      <th>browser</th>\n",
       "      <th>yes</th>\n",
       "      <th>no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>exposed</td>\n",
       "      <td>2020-07-10</td>\n",
       "      <td>8</td>\n",
       "      <td>generic smartphone</td>\n",
       "      <td>chrome mobile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>exposed</td>\n",
       "      <td>2020-07-07</td>\n",
       "      <td>10</td>\n",
       "      <td>generic smartphone</td>\n",
       "      <td>chrome mobile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>exposed</td>\n",
       "      <td>2020-07-05</td>\n",
       "      <td>2</td>\n",
       "      <td>e5823</td>\n",
       "      <td>chrome mobile webview</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>control</td>\n",
       "      <td>2020-07-03</td>\n",
       "      <td>15</td>\n",
       "      <td>samsung sm-a705fn</td>\n",
       "      <td>facebook</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>control</td>\n",
       "      <td>2020-07-03</td>\n",
       "      <td>15</td>\n",
       "      <td>generic smartphone</td>\n",
       "      <td>chrome mobile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  experiment        date  hour         device_make                browser  \\\n",
       "0    exposed  2020-07-10     8  generic smartphone          chrome mobile   \n",
       "1    exposed  2020-07-07    10  generic smartphone          chrome mobile   \n",
       "2    exposed  2020-07-05     2               e5823  chrome mobile webview   \n",
       "3    control  2020-07-03    15   samsung sm-a705fn               facebook   \n",
       "4    control  2020-07-03    15  generic smartphone          chrome mobile   \n",
       "\n",
       "   yes  no  \n",
       "0    0   0  \n",
       "1    0   0  \n",
       "2    0   1  \n",
       "3    0   0  \n",
       "4    0   0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mod.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "887d4e44",
   "metadata": {},
   "source": [
    "### Note 2\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    \n",
    "1. Data does not contain any missing values, therefore all 8077 observations can be used.\n",
    "\n",
    "</div>\n",
    "\n",
    "[TOP](#Executive-Summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de9428c0",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "### Date Plot\n",
    "- The experiment was run between `July 3-10, 2020, (Friday to Friday)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "feec7d2f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date\n",
      "2020-07-03    2015\n",
      "2020-07-04     903\n",
      "2020-07-05     890\n",
      "2020-07-06     490\n",
      "2020-07-07     480\n",
      "2020-07-08    1198\n",
      "2020-07-09    1208\n",
      "2020-07-10     893\n",
      "Name: date, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Experiment Dates\n",
    "# Group by date\n",
    "df_dates = df_mod.groupby('date')['date'].count()\n",
    "print(df_dates)\n",
    "type(df_dates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bee2f57b",
   "metadata": {},
   "source": [
    "### Barplot of dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10c587aa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_date = {'Date': ['2020-07-03', '2020-07-04', '2020-07-05', '2020-07-06', '2020-07-07', '2020-07-08', '2020-07-09', '2020-07-10'],\n",
    "           'Counts': [2015, 903, 890, 490, 480, 1198, 1208, 893]}\n",
    "\n",
    "plt.bar(df_date['Date'], df_date['Counts'], color='green', edgecolor='black')\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Counts Per Day Of Testing', fontsize=18)\n",
    "plt.ylabel('Count', fontsize=16)\n",
    "plt.xlabel('Day', fontsize=16)\n",
    "plt.text(-0.7, 2040, \"2015, Friday\")\n",
    "plt.text(6.7, 950, \" 893\\nFriday\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aec3254f",
   "metadata": {},
   "source": [
    "### Note 3\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    \n",
    "1. Friday, July 3rd, 2020 traffic = 2015 impressions\n",
    "\n",
    "2. Friday, July 10th, 2020 traffic = 815 impressions\n",
    "\n",
    "3. The 247% greater traffic on 7/3 versus 7/10 may be a *novelty* effect shown on the first day.\n",
    "    \n",
    "</div>\n",
    "\n",
    "[TOP](#Executive-Summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e03f5206",
   "metadata": {},
   "source": [
    "### Histogram for Hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e9a9ca93",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hour\n",
      "0      194\n",
      "1      222\n",
      "2      230\n",
      "3      266\n",
      "4      281\n",
      "5      302\n",
      "6      327\n",
      "7      381\n",
      "8      394\n",
      "9      346\n",
      "10     336\n",
      "11     282\n",
      "12     278\n",
      "13     290\n",
      "14     319\n",
      "15    1895\n",
      "16     335\n",
      "17     263\n",
      "18     273\n",
      "19     227\n",
      "20     264\n",
      "21     206\n",
      "22     135\n",
      "23      31\n",
      "Name: hour, dtype: int64\n",
      "\n",
      "Mean over 24 hours = 336.5416666666667\n"
     ]
    }
   ],
   "source": [
    "# Experiment Hour\n",
    "# Group by Hour\n",
    "df_hour = df_mod.groupby('hour')['hour'].count()\n",
    "print(df_hour)\n",
    "type(df_hour)\n",
    "print('\\nMean over 24 hours =', df_hour.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ff6361a",
   "metadata": {},
   "source": [
    "### Histogram of Hours Vs Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d5ba79a3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.07, 'Mean = 336 Impressions / hr')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of Hours Vs Counts\n",
    "df_hour = df_mod['hour']\n",
    "df_hour.plot(kind='hist', color='green',\n",
    "             density=True, edgecolor='black', bins=24)\n",
    "plt.title('Counts Per Hour Of Testing', fontsize=18)\n",
    "plt.xlabel('Hour', fontsize=16)\n",
    "plt.ylabel('Proportion', fontsize=16)\n",
    "plt.text(4, 0.23, \"1895 Impressions @ 15:00\")\n",
    "plt.text(0, 0.07, \"Mean = 336 Impressions / hr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19fb7c58",
   "metadata": {},
   "source": [
    "### Note 4\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    \n",
    "1. 1895 impressions were collected at hour 15.\n",
    "\n",
    "2. The mean of 24 hours is 336 impressions.\n",
    "\n",
    "3. The **564%** greater traffic on **hr=1500** over the **mean=336** may need to be further investigated.\n",
    "    \n",
    "</div>\n",
    "\n",
    "\n",
    "[TOP](#Executive-Summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32d50b11",
   "metadata": {},
   "source": [
    "### Browser Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "85678042",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create text variable\n",
    "text1 = \" \".join(browser for browser in df_mod.browser)\n",
    "\n",
    "# Creatd word_cloud\n",
    "word_cloud1 = WordCloud(collocations=False, \n",
    "                        background_color='white',\n",
    "                        width=3000, \n",
    "                        height=1500).generate(text1)\n",
    "# Save image\n",
    "word_cloud1.to_file('figures/browser_wordcloud.png')\n",
    "\n",
    "# Display Word Cloud\n",
    "plt.imshow(word_cloud1, interpolation='none')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c08147e",
   "metadata": {},
   "source": [
    "### Device Make Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3732420d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the text variable\n",
    "text1 = \" \".join(device_make for device_make in df_mod.device_make)\n",
    "\n",
    "# Create word_cloud\n",
    "word_cloud1 = WordCloud(collocations=False, \n",
    "                        background_color='white',\n",
    "                        width=3000, \n",
    "                        height=1500).generate(text1)\n",
    "# Save image\n",
    "word_cloud1.to_file('figures/device_wordcloud.png')\n",
    "\n",
    "# Display Word Cloud\n",
    "plt.imshow(word_cloud1, interpolation='none')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80653e99",
   "metadata": {},
   "source": [
    "[TOP](#Executive-Summary)\n",
    "\n",
    "## Statistical Tests & Analysis\n",
    "\n",
    "### Fisher's Exact Test\n",
    "\n",
    "Fisher's Exact Test is a Non-Parametric test. In plain English, Fisher's test does not make the same assumptions as the z-test. In fact, Fisher's test calculates the exact probabality of an occurence. \n",
    "\n",
    "Fisher's test assumes:\n",
    "\n",
    "1. Random sampling\n",
    "2. Independence\n",
    "3. Mutually exclusive groups\n",
    "\n",
    "It is appropriate to use Fisher's Exact Test when:\n",
    "\n",
    "1. Variables are categorical and the result is classified in two different groups.\n",
    "1. One is looking for the relationship between two variables.\n",
    "1. The response variables are recorded using counts.\n",
    "\n",
    "Fisher’s Exact Test uses the hypotheses test:\n",
    "\n",
    "- H0: The two variables are independent.\n",
    "- H1: The two variables are not independent, \n",
    "  - i.e. the difference in conversion rates is related to using the different icons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c9e3bd2b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dimensions of Participants dataframe = (1243, 3)\n"
     ]
    },
    {
     "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>experiment</th>\n",
       "      <th>yes</th>\n",
       "      <th>no</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>exposed</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>exposed</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>exposed</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>control</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>control</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   experiment  yes  no\n",
       "2     exposed    0   1\n",
       "16    exposed    1   0\n",
       "20    exposed    0   1\n",
       "23    control    1   0\n",
       "27    control    0   1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KEEP 'PARTICIPATING' MEMBERS ONLY\n",
    "# Where 'yes' or 'no' columns are equal to 1.\n",
    "\n",
    "# REMOVE columns [[0,2,3,4,5,6]] from ORIGINAL dataframe, **df**\n",
    "df_mod = df.drop(df.columns[[0,2,3,4,5,6]], axis=1, inplace=False)\n",
    "\n",
    "# KEEP PARTICIPATING MEMBERS ONLY\n",
    "df_participants = df_mod[(df_mod['yes'] == 1) | (df_mod['no'] == 1)]\n",
    "print('\\nDimensions of Participants dataframe =', df_participants.shape)\n",
    "\n",
    "df_participants.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0725ea0c",
   "metadata": {
    "code_folding": [
     0,
     3,
     6,
     9
    ],
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fishers_a = df_participants[(df_participants['experiment'] == 'control') & \n",
    "                            (df_participants['no'] == 0)].count()\n",
    "\n",
    "fishers_b = df_participants[(df_participants['experiment'] == 'control') & \n",
    "                            (df_participants['no'] == 1)].count()\n",
    "\n",
    "fishers_c = df_participants[(df_participants['experiment'] == 'exposed') & \n",
    "                            (df_participants['no'] == 0)].count()\n",
    "\n",
    "fishers_d = df_participants[(df_participants['experiment'] == 'exposed') & \n",
    "                            (df_participants['no'] == 1)].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9c2eb7a0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fishers_a = 264\n",
      "fishers_b = 322\n",
      "fishers_c = 308\n",
      "fishers_d = 349\n"
     ]
    }
   ],
   "source": [
    "# Fisher 2x2 Contingency Table\n",
    "\n",
    "print('fishers_a =', fishers_a[0])\n",
    "print('fishers_b =', fishers_b[0])\n",
    "print('fishers_c =', fishers_c[0])\n",
    "print('fishers_d =', fishers_d[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8b15ada1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "exposed    657\n",
       "control    586\n",
       "Name: experiment, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_participants['experiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cdb66e9f",
   "metadata": {
    "scrolled": true
   },
   "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>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Control</th>\n",
       "      <td>264</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exposed</th>\n",
       "      <td>308</td>\n",
       "      <td>349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          No  Yes\n",
       "Control  264  322\n",
       "Exposed  308  349"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counts\n",
    "\n",
    "df = pd.DataFrame({'No': [264, 308], 'Yes': [322, 349]},\n",
    "                  index=pd.Index(['Control', 'Exposed']))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "07cd366e",
   "metadata": {
    "scrolled": true
   },
   "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>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Control</th>\n",
       "      <td>0.212</td>\n",
       "      <td>0.259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exposed</th>\n",
       "      <td>0.248</td>\n",
       "      <td>0.281</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            No    Yes\n",
       "Control  0.212  0.259\n",
       "Exposed  0.248  0.281"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Proportions Table\n",
    "\n",
    "df_prop = pd.DataFrame({'No': [0.212, 0.248], 'Yes': [0.259, 0.281]},\n",
    "                       index=pd.Index(['Control', 'Exposed']))\n",
    "df_prop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "06c19dec",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Odds ratio is: 0.9290150842945873\n",
      "\n",
      "P-value is: 0.5309716576381456\n"
     ]
    }
   ],
   "source": [
    "# Fishers exact test on the data\n",
    "\n",
    "odds_ratio, p_value = stats.fisher_exact(df, alternative=\"two-sided\")\n",
    "print(\"\\nOdds ratio is: \" + str(odds_ratio))\n",
    "print(\"\\nP-value is: \" + str(p_value))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d5f1c2b",
   "metadata": {},
   "source": [
    "### Results of Fisher's Exact Test\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "\n",
    "1. P-value = 0.531, therefore this result will occur on average 53% of the time.\n",
    "    \n",
    "2. Fisher's test also produces an odds ratio for betting people. Using these icons, the odds of increasing the conversion rates are 0.93 : 1.0.\n",
    "    - In other words, the odds of increasing sales is ~1:1, no change.\n",
    "\n",
    "</div>\n",
    "\n",
    "[TOP](#Executive-Summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c5481fb",
   "metadata": {},
   "source": [
    "### Phi Coefficient of Association\n",
    "\n",
    "The Phi Coefficient of Association ($\\large \\Phi$) is a measure of the degree of association (cooperative effect) between two binary variables.  \n",
    "\n",
    "The Phi Coefficient test can be interpreted similarly to Pearson's correlation coefficient. Chiefly, does any change in conversion rate positively (or negatively) correlate with changing the two icons?\n",
    "\n",
    "   - H0: There IS NO statistically significant relationship between the change in conversion rate and changing the cat and dog icons.\n",
    " \n",
    "   - H1: There IS a statistically significant relationship between the two variables.\n",
    "\n",
    "It is appropriate to use Phi in the following scenario:\n",
    "\n",
    "1. To determine the relationship between two variables\n",
    "\n",
    "\n",
    "2. The variables of interest are binary\n",
    "\n",
    "\n",
    "3. There are only two variables\n",
    "\n",
    "\n",
    "$$\\large \\Phi = \\frac{a \\cdot d - b \\cdot c}{\\sqrt{efgh}}$$ \n",
    "\n",
    "Where: \n",
    "\n",
    "| Experiment |       NO |     YES |    Sums |\n",
    "|-----------:|---------:|--------:|--------:|\n",
    "| Control    |  a = 264 | b = 322 | e = 586 |\n",
    "| Exposed    |  c = 308 | d = 349 | f = 657 |\n",
    "| Sums       |  g = 572 | h = 671 |    1243 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7bfb2833",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The numerator of Phi = -7040\n",
      "\n",
      "The denominator of Phi = 384406\n",
      "\n",
      "Phi Coefficient = -0.018313944421528762\n"
     ]
    }
   ],
   "source": [
    "import math \n",
    "\n",
    "numerator = (264 * 349)-(308 * 322)\n",
    "\n",
    "denominator = ((586)*(657)*(572)*(671))**(0.5)\n",
    "\n",
    "print('\\nThe numerator of Phi =', numerator)\n",
    "print('\\nThe denominator of Phi =', math.floor(denominator))\n",
    "print('\\nPhi Coefficient =', numerator/denominator)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b616351",
   "metadata": {},
   "source": [
    "$$\\large \\Phi = \\frac{-7,040}{384,406} = -0.0183$$\n",
    "\n",
    "The Phi Coefficient test is interpreted similarly to Pearson's correlation coefficient\n",
    "\n",
    "The Phi Coefficient takes on values between -1 and 1 where:\n",
    "   - -1 indicates a perfectly negative relationship between the two variables.\n",
    "   - 0 indicates no association between the two variables.\n",
    "   - 1 indicates a perfectly positive relationship between the two variables.\n",
    "\n",
    "\n",
    "### Results of Phi Coefficient of Association\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "\n",
    "1. Since Phi = -0.0183, there is no relationship between the the icons and increased sales.\n",
    "    - The Phi Coefficient is very close to zero, there is <strong>no link</strong> between the icons and increased sales.\n",
    "    \n",
    "<div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6b77189",
   "metadata": {},
   "source": [
    "[TOP](#Executive-Summary)\n",
    "\n",
    "### Two Sample Z-test\n",
    "\n",
    ">This test uses a simple normal test for proportions. It should be the same as running the mean z-test on the data encoded 1 for event and 0 for no event so that the sum corresponds to the count.\n",
    ">\n",
    ">In the one and two sample cases with two-sided alternative, this test produces the same p-value as proportions_chisquare, since the chisquare is the distribution of the square of a standard normal distribution.\n",
    ">\n",
    ">https://www.statsmodels.org/stable/generated/statsmodels.stats.proportion.proportions_ztest.html\n",
    "\n",
    "\n",
    "- H0: $\\mu = \\mu_0$\n",
    "- HA: $\\mu \\neq \\mu_0$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "83dab72c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z-score = 0.646\n",
      "P-value = 0.518\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from statsmodels.stats.proportion import proportions_ztest\n",
    "\n",
    "count = np.array([322, 349])\n",
    "\n",
    "nobs = np.array([586, 657])\n",
    "\n",
    "stat, pval = proportions_ztest(count, nobs)\n",
    "\n",
    "print('Z-score =', '{0:0.3f}'.format(stat))\n",
    "print('P-value =', '{0:0.3f}'.format(pval))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "186f6b7f",
   "metadata": {},
   "source": [
    "### Results for the Z-test\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "\n",
    "1. Since the P-value = 0.518, the proportions are equal. There is no difference between the two conversion rates.\n",
    " \n",
    "</div>\n",
    "\n",
    "[TOP](#Executive-Summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f6dfde9",
   "metadata": {},
   "source": [
    "### Ratios of Respondents to total & Exposed to Control"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1515215",
   "metadata": {},
   "source": [
    "**Conversion Rate**: Conversion rate, defined as the proportion of sessions ending up with a transaction.\n",
    "\n",
    "Conversion Rate = # of converted / total number $\\cdot$ 100\\%\n",
    "\n",
    "\n",
    "| Experiment |       NO |     YES |    Sums |\n",
    "|-----------:|---------:|--------:|--------:|\n",
    "| Control    |  a = 264 | b = 322 | e = 586 |\n",
    "| Exposed    |  c = 308 | d = 349 | f = 657 |\n",
    "| Sums       |  g = 572 | h = 671 |    1243 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "556c0772",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.15389377244026248, 0.5494880546075085, 0.5312024353120244)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1243/8077, 322/586, 349/657"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fdf5ea8",
   "metadata": {},
   "source": [
    "| Class | Proportion |\n",
    "|:------|-----------:|\n",
    "| Participants vs Total Impressions ratio | 15.4% |\n",
    "| Control: Conversion-Ratio of Cat-people | 54.9% |\n",
    "| Exposed: Conversion-Ratio of Dog-people | 53.1% |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5861861c",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3ecb1be",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "\n",
    "1. No changes to www.catsVSdogs.com should be made at this time.   \n",
    "<br>\n",
    "    \n",
    "1. Fisher's Exact Test (P-value = 0.531) suggests there is no association between the two icons and any change of conversion rates. These results will occur 53% of the time in a random trial.  \n",
    "<br>\n",
    "    \n",
    "2. Phi Coefficient of Association ($\\Phi$ = -0.0183) suggests there is no relationship between the two icons and any change of conversion rates.  \n",
    "<br> \n",
    "    \n",
    "3. Z-test (P-value = 0.518) the proportions are equal. There is no difference between the two conversion rates.\n",
    "<br>\n",
    "    \n",
    "4. The **564% greater traffic on hr=1500 versus the mean=336**, as seen on the Hour histogram, could be a serious problem and may need to be investigated.\n",
    "       \n",
    "</div>\n",
    "\n",
    "[TOP](#Executive-Summary)"
   ]
  }
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