AB - Testing/AB - test.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"-NJ7HkA5B8nJ"},"source":["## <center> **A/B Testing on Advertisement Dataset.**</center>\n"]},{"cell_type":"markdown","metadata":{},"source":["The SmartAd company expects that the design of a new advertising distribution strategy can increase the success rate of ads from 45% to 50%. \n","\n","The new strategy involves providing creative and interactive online ads with the SmartAd brand, while the old strategy involves providing dummy ads.\n"]},{"cell_type":"markdown","metadata":{},"source":["### **Formulating Hypotheses**"]},{"cell_type":"markdown","metadata":{},"source":["#### **Null Hypothesis**\n","\n","$$H_0: p = p_0$$ \n","\n","\"There is no significant difference in the success rate of ads between the two groups.\"\n"]},{"cell_type":"markdown","metadata":{},"source":["#### **Alternative Hypothesis** \n","\n","$$H_a: p \\ne p_0$$ \n","\n","\"There is a significant difference in the success rate of ads between the two groups.\"\n"]},{"cell_type":"markdown","metadata":{},"source":["Given the uncertainty of whether the new design will perform *\"better/worse/same\"* as the current design, a two-tailed test will be conducted with a confidence Level of 95% i.e. (α = 0.05).\n","\n","p and pₒ represent the conversion rate of the old design and the new design, with a confidence level of 95%.\n","\n","$$\\alpha = 0.05$$\n"]},{"cell_type":"markdown","metadata":{"id":"lUY4-CopB8nN"},"source":["### **1. Importing Libraries.**"]},{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2022-10-19T09:30:16.421982Z","iopub.status.busy":"2022-10-19T09:30:16.421433Z","iopub.status.idle":"2022-10-19T09:30:16.431216Z","shell.execute_reply":"2022-10-19T09:30:16.429940Z","shell.execute_reply.started":"2022-10-19T09:30:16.421934Z"},"executionInfo":{"elapsed":23,"status":"ok","timestamp":1679405190182,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"AdlaGr_2B8nN","trusted":true},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","import statsmodels.stats.api as sms\n","import scipy.stats as stats\n","from statsmodels.stats.proportion import proportions_ztest, proportion_confint"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["import requests\n","import warnings\n","warnings.simplefilter(action=\"ignore\", category=FutureWarning)"]},{"cell_type":"markdown","metadata":{"id":"2wHZuk07B8nO"},"source":["### **2. Data Preparation**"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["data_url = \"https://raw.githubusercontent.com/Lulu-L2/MyDataset/main/AdSmartABdata%20-%20AdSmartABdata.csv\"\n","\n","response = requests.get(data_url)"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":["with open(\"smart_ad_data.csv\", \"wb\") as file:\n","    file.write(response.content)"]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2022-10-19T09:30:16.765959Z","iopub.status.busy":"2022-10-19T09:30:16.765393Z","iopub.status.idle":"2022-10-19T09:30:16.807282Z","shell.execute_reply":"2022-10-19T09:30:16.805447Z","shell.execute_reply.started":"2022-10-19T09:30:16.765883Z"},"executionInfo":{"elapsed":22,"status":"ok","timestamp":1679405190182,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"sE5dnMY0B8nO","trusted":true},"outputs":[],"source":["df = pd.read_csv('smart_ad_data.csv',parse_dates=['date'])"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"execution":{"iopub.execute_input":"2022-10-19T09:30:16.870542Z","iopub.status.busy":"2022-10-19T09:30:16.869982Z","iopub.status.idle":"2022-10-19T09:30:16.893335Z","shell.execute_reply":"2022-10-19T09:30:16.891180Z","shell.execute_reply.started":"2022-10-19T09:30:16.870492Z"},"executionInfo":{"elapsed":23,"status":"ok","timestamp":1679405190183,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"WiHXjEdHB8nP","outputId":"7e950484-0231-40d4-9bf8-aff39a05633d","trusted":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>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":6,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 8077 entries, 0 to 8076\n","Data columns (total 9 columns):\n"," #   Column       Non-Null Count  Dtype         \n","---  ------       --------------  -----         \n"," 0   auction_id   8077 non-null   object        \n"," 1   experiment   8077 non-null   object        \n"," 2   date         8077 non-null   datetime64[ns]\n"," 3   hour         8077 non-null   int64         \n"," 4   device_make  8077 non-null   object        \n"," 5   platform_os  8077 non-null   int64         \n"," 6   browser      8077 non-null   object        \n"," 7   yes          8077 non-null   int64         \n"," 8   no           8077 non-null   int64         \n","dtypes: datetime64[ns](1), int64(4), object(4)\n","memory usage: 568.0+ KB\n"]}],"source":["df.info()"]},{"cell_type":"markdown","metadata":{},"source":["#### **Column Descriptions**\n","\n","1. **auction_id**: Unique identifier for online users displayed on BIO. In standard terminology, this is referred to as the impression id. Users can view the BIO questionnaire but may choose not to respond. In this case, both the yes and no columns are set to zero.\n","\n","2. **experiment**: The group to which the user belongs - control or exposed.\n","    * control: Users shown dummy ads.\n","    * exposed: Users shown online interactive ads with the SmartAd brand.\n","    \n","3. **date**: Date in the format YYYY-MM-DD.\n","\n","4. **hour**: Hour of the day in the format HH.\n","\n","5. **device_make**: Name of the type of device owned by the user, e.g., Samsung.\n","\n","6. **platform_os**: OS id owned by the user.\n","\n","7. **browser**: Name of the browser used by the user to view the BIO questionnaire.\n","\n","8. **yes**: 1 if the user selected the \"Yes\" radio button for the BIO questionnaire.\n","\n","9. **no**: 1 if the user selected the \"No\" radio button for the BIO questionnaire."]},{"cell_type":"markdown","metadata":{"id":"Krkby9iWB8nQ"},"source":["#### **Missing Null Values**"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2022-10-19T09:30:17.276811Z","iopub.status.busy":"2022-10-19T09:30:17.276381Z","iopub.status.idle":"2022-10-19T09:30:17.290871Z","shell.execute_reply":"2022-10-19T09:30:17.289933Z","shell.execute_reply.started":"2022-10-19T09:30:17.276774Z"},"executionInfo":{"elapsed":22,"status":"ok","timestamp":1679405190183,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"d9GS5QAqB8nQ","outputId":"16ca1f4c-f274-4a35-a622-f0771a13f1fa","trusted":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":8,"metadata":{},"output_type":"execute_result"}],"source":["df.isnull().sum()"]},{"cell_type":"markdown","metadata":{"id":"z90I49fQB8nR"},"source":["#### **Data Preprocessing**"]},{"cell_type":"markdown","metadata":{"id":"ke45l4H0B8nR"},"source":["Before proceeding and taking a data sample to obtain our subset, we will ensure that no users have been sampled multiple times."]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2022-10-19T09:30:17.907145Z","iopub.status.busy":"2022-10-19T09:30:17.906700Z","iopub.status.idle":"2022-10-19T09:30:17.922154Z","shell.execute_reply":"2022-10-19T09:30:17.920284Z","shell.execute_reply.started":"2022-10-19T09:30:17.907104Z"},"executionInfo":{"elapsed":20,"status":"ok","timestamp":1679405190183,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"JZ8Ee58ZB8nR","outputId":"f0eca327-90dd-4fb4-ae77-bf8f47085e74","trusted":true},"outputs":[],"source":["session_counts = df['auction_id'].value_counts(ascending=False)\n","multi_users = session_counts[session_counts > 1].count()"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["There are 0 users that appear two times or more in the dataset\n"]}],"source":["print(f\"There are {multi_users} users that appear two times or more in the dataset\")"]},{"cell_type":"markdown","metadata":{"id":"c6LrOjodB8nR"},"source":["#### **Removing non-answers from the dataframe.**\n","\n","Non-answers (where the 'yes' and 'no' columns are both 0) represent users who did not fall into either category. \n","\n","While this may eliminate a significant percentage of observations, non-answers are not useful for our analysis since we cannot conclude whether the ad was successful or not. \n","\n","We will also consolidate the 'yes' and 'no' columns into a single column where \"1 == ad_success\" and \"0 == ad_failure\"."]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2022-10-19T09:30:18.372282Z","iopub.status.busy":"2022-10-19T09:30:18.371877Z","iopub.status.idle":"2022-10-19T09:30:18.382588Z","shell.execute_reply":"2022-10-19T09:30:18.381114Z","shell.execute_reply.started":"2022-10-19T09:30:18.372250Z"},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1679405190183,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"sAbgNWl5B8nR","outputId":"049c2e4b-5b43-4d6c-ca58-7f129f5f8512","trusted":true},"outputs":[{"data":{"text/plain":["0    7505\n","1     572\n","Name: yes, dtype: int64"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["df['yes'].value_counts()"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2022-10-19T09:30:18.585156Z","iopub.status.busy":"2022-10-19T09:30:18.584757Z","iopub.status.idle":"2022-10-19T09:30:18.596700Z","shell.execute_reply":"2022-10-19T09:30:18.595152Z","shell.execute_reply.started":"2022-10-19T09:30:18.585124Z"},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1679405190183,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"ZfIG646JB8nS","outputId":"80fc66a6-24b5-49de-93f2-8ce4460a864a","trusted":true},"outputs":[{"data":{"text/plain":["0    7406\n","1     671\n","Name: no, dtype: int64"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["df['no'].value_counts()"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"execution":{"iopub.execute_input":"2022-10-19T09:30:19.090614Z","iopub.status.busy":"2022-10-19T09:30:19.089727Z","iopub.status.idle":"2022-10-19T09:30:19.120740Z","shell.execute_reply":"2022-10-19T09:30:19.119306Z","shell.execute_reply.started":"2022-10-19T09:30:19.090556Z"},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1679405190183,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"qidOFsytB8nS","outputId":"1c7675a7-7f5e-4e0b-a08b-a3a1dccef125","trusted":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>no</th>\n","      <th>0</th>\n","      <th>1</th>\n","    </tr>\n","    <tr>\n","      <th>yes</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>6834</td>\n","      <td>671</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>572</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["no      0    1\n","yes           \n","0    6834  671\n","1     572    0"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["pd.crosstab(df['yes'],df['no'])"]},{"cell_type":"markdown","metadata":{"id":"ydzy60O4B8nS"},"source":["There are **\"6834\"** users who do not fall into either the 'yes' or 'no' category, and therefore, they will not contribute to the results of this study. \n","\n","Hence, it is necessary to remove the data of users who do not belong to either of these two categories."]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1679405190183,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"jQFjOtDqVS3m","outputId":"89db142b-2083-44c4-b34a-42a145191c97"},"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>yes</th>\n","      <th>no</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>8072</th>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8073</th>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8074</th>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8075</th>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8076</th>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>8077 rows × 2 columns</p>\n","</div>"],"text/plain":["      yes  no\n","0       0   0\n","1       0   0\n","2       0   1\n","3       0   0\n","4       0   0\n","...   ...  ..\n","8072    0   0\n","8073    0   0\n","8074    0   0\n","8075    0   0\n","8076    0   0\n","\n","[8077 rows x 2 columns]"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["df[['yes','no']]"]},{"cell_type":"code","execution_count":15,"metadata":{"execution":{"iopub.execute_input":"2022-10-19T09:30:19.510205Z","iopub.status.busy":"2022-10-19T09:30:19.509368Z","iopub.status.idle":"2022-10-19T09:30:19.539056Z","shell.execute_reply":"2022-10-19T09:30:19.535278Z","shell.execute_reply.started":"2022-10-19T09:30:19.510136Z"},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1679405190184,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"qEuzZlIIB8nS","trusted":true},"outputs":[],"source":["if_ad_success = []\n","\n","for x, y in zip(df.yes, df.no):\n","    if (x == 1) and (y == 0):\n","        if_ad_success.append(1)\n","    elif (x == 0) and (y == 1):\n","        if_ad_success.append(0)\n","    else:\n","        if_ad_success.append('no_response')"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[],"source":["df['if_ad_success'] = if_ad_success"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[],"source":["df = df.loc[~df.if_ad_success.isin(['no_response'])]\n","df = df.drop(['yes', 'no'], axis = 1)"]},{"cell_type":"markdown","metadata":{"id":"nBfgM7A-B8nS"},"source":["#### **Exploring features : `device_make`, `browser` & `platform_os`.**"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2022-10-19T09:30:20.346189Z","iopub.status.busy":"2022-10-19T09:30:20.344936Z","iopub.status.idle":"2022-10-19T09:30:20.357120Z","shell.execute_reply":"2022-10-19T09:30:20.355846Z","shell.execute_reply.started":"2022-10-19T09:30:20.346131Z"},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1679405190184,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"Jic64r7hB8nS","outputId":"538664ba-3dc7-43ec-cf35-be9171495afb","trusted":true},"outputs":[{"data":{"text/plain":["Generic Smartphone    719\n","Samsung SM-G960F       45\n","Samsung SM-G950F       35\n","Samsung SM-G973F       22\n","Samsung SM-A202F       18\n","iPhone                 18\n","Samsung SM-G930F       16\n","Samsung SM-G975F       15\n","Samsung SM-A405FN      14\n","Samsung SM-A105FN      13\n","Name: device_make, dtype: int64"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["df['device_make'].value_counts()[:10]"]},{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2022-10-19T09:30:20.604176Z","iopub.status.busy":"2022-10-19T09:30:20.602865Z","iopub.status.idle":"2022-10-19T09:30:20.614500Z","shell.execute_reply":"2022-10-19T09:30:20.613329Z","shell.execute_reply.started":"2022-10-19T09:30:20.604125Z"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1679405190184,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"58MMgzePB8nT","outputId":"945016e0-4764-4e02-c541-46a1e946ecf7","trusted":true},"outputs":[{"data":{"text/plain":["Chrome Mobile                 695\n","Chrome Mobile WebView         227\n","Facebook                      156\n","Samsung Internet              145\n","Mobile Safari                  14\n","Mobile Safari UI/WKWebView      3\n","Chrome                          2\n","Chrome Mobile iOS               1\n","Name: browser, dtype: int64"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["df['browser'].value_counts()"]},{"cell_type":"code","execution_count":20,"metadata":{},"outputs":[{"data":{"text/plain":["6    1226\n","5      17\n","Name: platform_os, dtype: int64"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["df[\"platform_os\"].value_counts()"]},{"cell_type":"markdown","metadata":{},"source":["**Codifying the Device List.**"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"execution":{"iopub.execute_input":"2022-10-19T09:30:20.882179Z","iopub.status.busy":"2022-10-19T09:30:20.881387Z","iopub.status.idle":"2022-10-19T09:30:20.910273Z","shell.execute_reply":"2022-10-19T09:30:20.908621Z","shell.execute_reply.started":"2022-10-19T09:30:20.882127Z"},"executionInfo":{"elapsed":14,"status":"ok","timestamp":1679405190184,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"DjcPa2-uB8nT","outputId":"300b1213-80a6-45d7-e2c7-18add36f240a","trusted":true},"outputs":[],"source":["device_list_codified = []\n","\n","for x in df.device_make:\n","    if 'Samsung' in x:\n","        device_list_codified.append(1)\n","    elif 'iPhone' in x:\n","        device_list_codified.append(2)\n","    else:\n","        device_list_codified.append(0)"]},{"cell_type":"markdown","metadata":{},"source":["**Codifying the Browser List.**"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[],"source":["browser_list_codified = []\n","\n","for x in df.browser:\n","    if 'Chrome' in x:\n","        browser_list_codified.append(1)\n","    elif 'Safari' in x:\n","        browser_list_codified.append(2)\n","    else:\n","        browser_list_codified.append(0)"]},{"cell_type":"code","execution_count":23,"metadata":{},"outputs":[],"source":["df.device_make = device_list_codified\n","df.browser = browser_list_codified"]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 1243 entries, 2 to 8071\n","Data columns (total 8 columns):\n"," #   Column         Non-Null Count  Dtype         \n","---  ------         --------------  -----         \n"," 0   auction_id     1243 non-null   object        \n"," 1   experiment     1243 non-null   object        \n"," 2   date           1243 non-null   datetime64[ns]\n"," 3   hour           1243 non-null   int64         \n"," 4   device_make    1243 non-null   int64         \n"," 5   platform_os    1243 non-null   int64         \n"," 6   browser        1243 non-null   int64         \n"," 7   if_ad_success  1243 non-null   object        \n","dtypes: datetime64[ns](1), int64(4), object(3)\n","memory usage: 87.4+ KB\n"]}],"source":["df.info()"]},{"cell_type":"code","execution_count":25,"metadata":{},"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>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>if_ad_success</th>\n","    </tr>\n","  </thead>\n","  <tbody>\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>0</td>\n","      <td>6</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>008aafdf-deef-4482-8fec-d98e3da054da</td>\n","      <td>exposed</td>\n","      <td>2020-07-04</td>\n","      <td>16</td>\n","      <td>0</td>\n","      <td>6</td>\n","      <td>1</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>00a1384a-5118-4d1b-925b-6cdada50318d</td>\n","      <td>exposed</td>\n","      <td>2020-07-06</td>\n","      <td>8</td>\n","      <td>0</td>\n","      <td>6</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>00b6fadb-10bd-49e3-a778-290da82f7a8d</td>\n","      <td>control</td>\n","      <td>2020-07-08</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>00ebf4a8-060f-4b99-93ac-c62724399483</td>\n","      <td>control</td>\n","      <td>2020-07-03</td>\n","      <td>15</td>\n","      <td>0</td>\n","      <td>6</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                              auction_id experiment       date  hour  \\\n","2   0016d14a-ae18-4a02-a204-6ba53b52f2ed    exposed 2020-07-05     2   \n","16  008aafdf-deef-4482-8fec-d98e3da054da    exposed 2020-07-04    16   \n","20  00a1384a-5118-4d1b-925b-6cdada50318d    exposed 2020-07-06     8   \n","23  00b6fadb-10bd-49e3-a778-290da82f7a8d    control 2020-07-08     4   \n","27  00ebf4a8-060f-4b99-93ac-c62724399483    control 2020-07-03    15   \n","\n","    device_make  platform_os  browser if_ad_success  \n","2             0            6        1             0  \n","16            0            6        1             1  \n","20            0            6        1             0  \n","23            1            6        0             1  \n","27            0            6        1             0  "]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["del if_ad_success, device_list_codified, browser_list_codified\n","\n","df.head()"]},{"cell_type":"markdown","metadata":{"id":"3fbd-JVhB8nT"},"source":["### **3. Data Visualization**"]},{"cell_type":"code","execution_count":26,"metadata":{},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAmQAAAE+CAYAAAAj7AywAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAZqUlEQVR4nO3df7DddX3n8eeLBFAELBkCCwk01Emx4C+WwKpUa4UW2l2b1BUbt9iMZRp3FxHd/ljY7SjjbmbsyLp1bHE2o7WhtWKKdsm6roKpiloFwg+FECmpsSQS4WrX320UfO8f55v1cLkJh+R+7+ecy/Mxc+d8z+d8vt/zvmfuvOd1v9/v+X5TVUiSJKmdQ1oXIEmS9GRnIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGFrYu4GAce+yxtWzZstZlSJpDt91229eranHrOmaDPUx6ctlf/5roQLZs2TK2bNnSugxJcyjJ37euYbbYw6Qnl/31Lw9ZSpIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktTYRN/LchRn/u41rUsYG7e97TdalyBJTd3/lme3LmFsnPymu1qXoCHuIZMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxnoNZEnemGRrkruTvD/JU5IsSnJjkvu6x2OG5l+RZHuSe5Oc32dtkiRJ46K3QJZkCfB6YEVVPQtYAKwGLgc2V9VyYHP3nCSnda+fDlwAXJ1kQV/1SZIkjYu+D1kuBJ6aZCFwBPAAsBLY0L2+AVjVLa8Erq2qPVW1A9gOnN1zfZIkSc31Fsiq6qvAVcD9wG7gW1V1A3B8Ve3u5uwGjutWWQLsHNrErm5MkiRpXuvzkOUxDPZ6nQKcCDwtyUX7W2WGsZphu2uTbEmyZWpqanaKlSRJaqjPQ5bnATuqaqqqfgh8CHgh8GCSEwC6x4e6+buAk4bWX8rgEOejVNX6qlpRVSsWL17cY/mSJElzo89Adj/w/CRHJAlwLrAN2ASs6easAa7vljcBq5McnuQUYDlwS4/1SZIkjYWFfW24qm5Och1wO/AwcAewHjgS2JjkYgah7cJu/tYkG4F7uvmXVNUjfdUnSZI0LnoLZABV9WbgzdOG9zDYWzbT/HXAuj5rkiRJGjdeqV+SJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkmaQ5I1Jtia5O8n7kzwlyaIkNya5r3s8Zmj+FUm2J7k3yfkta5c0eQxkkjRNkiXA64EVVfUsYAGwGrgc2FxVy4HN3XOSnNa9fjpwAXB1kgUtapc0mQxkkjSzhcBTkywEjgAeAFYCG7rXNwCruuWVwLVVtaeqdgDbgbPntlxJk8xAJknTVNVXgauA+4HdwLeq6gbg+Kra3c3ZDRzXrbIE2Dm0iV3dmCSNxEAmSdN054atBE4BTgSeluSi/a0yw1jtY9trk2xJsmVqaurgi5U0LxjIJOmxzgN2VNVUVf0Q+BDwQuDBJCcAdI8PdfN3AScNrb+UwSHOx6iq9VW1oqpWLF68uLdfQNJkMZBJ0mPdDzw/yRFJApwLbAM2AWu6OWuA67vlTcDqJIcnOQVYDtwyxzVLmmALWxcgSeOmqm5Och1wO/AwcAewHjgS2JjkYgah7cJu/tYkG4F7uvmXVNUjTYqXNJEMZJI0g6p6M/DmacN7GOwtm2n+OmBd33VJmp88ZClJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpsV4DWZKfSHJdki8l2ZbkBUkWJbkxyX3d4zFD869Isj3JvUnO77M2SZKkcdH3HrJ3AB+tqmcCzwW2AZcDm6tqObC5e06S04DVwOnABcDVSRb0XJ8kSVJzvQWyJEcDLwbeA1BVP6iqbwIrgQ3dtA3Aqm55JXBtVe2pqh3AduDsvuqTJEkaF33uIfspYAp4b5I7krw7ydOA46tqN0D3eFw3fwmwc2j9Xd2YJEnSvNZnIFsI/HPgXVV1BvA9usOT+5AZxuoxk5K1SbYk2TI1NTU7lUqSJDXUZyDbBeyqqpu759cxCGgPJjkBoHt8aGj+SUPrLwUemL7RqlpfVSuqasXixYt7K16SJGmu9BbIquprwM4kp3ZD5wL3AJuANd3YGuD6bnkTsDrJ4UlOAZYDt/RVnyRJ0rhY2PP2LwXel+Qw4MvAaxiEwI1JLgbuBy4EqKqtSTYyCG0PA5dU1SM91ydJktRcr4Gsqu4EVszw0rn7mL8OWNdnTZIkSePGK/VLkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJM0gyU8kuS7Jl5JsS/KCJIuS3Jjkvu7xmKH5VyTZnuTeJOe3rF3S5DGQSdLM3gF8tKqeCTwX2MbgbiObq2o5sLl7TpLTgNXA6cAFwNVJFjSpWtJEMpBJ0jRJjgZeDLwHoKp+UFXfBFYCG7ppG4BV3fJK4Nqq2lNVO4DtwNlzWbOkyWYgk6TH+ilgCnhvkjuSvDvJ04Djq2o3QPd4XDd/CbBzaP1d3ZgkjcRAJkmPtZDBvXffVVVnAN+jOzy5D5lhrGacmKxNsiXJlqmpqYOvVNK8YCCTpMfaBeyqqpu759cxCGgPJjkBoHt8aGj+SUPrLwUemGnDVbW+qlZU1YrFixf3UrykyWMgk6RpquprwM4kp3ZD5zK4z+4mYE03tga4vlveBKxOcniSU4DlwC1zWLKkCdf3zcUlaVJdCrwvyWHAl4HXMPgndmOSi4H7gQsBqmprko0MQtvDwCVV9UibsiVNIgOZJM2gqu4EVszw0rn7mL8OWNdnTZLmLw9ZSpIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNjRTIkmweZUySxo39S9IkWLi/F5M8BTgCODbJMUC6l44GTuy5Nkk6YPYvSZNkv4EMeC3wBgbN6zZ+3NC+Dfxxf2VJ0kGzf0maGPsNZFX1DuAdSS6tqnfOUU2SdNDsX5ImyePtIQOgqt6Z5IXAsuF1quqanuqSpFlh/5I0CUYKZEn+DHgGcCfwSDdcgA1N0lizf0maBCMFMmAFcFpVVZ/FSFIP7F+Sxt6o1yG7G/hnfRYiST2xf0kae6PuITsWuCfJLcCevYNV9Su9VCVJs8f+JWnsjRrIruyzCEnq0ZWtC5CkxzPqtyw/1XchktQH+5ekSTDqtyy/w+BbSQCHAYcC36uqo/sqTJJmg/1L0iQYdQ/ZUcPPk6wCzu6jIEmaTfYvSZNg1G9ZPkpV/U/gpbNbiiT1z/4laRyNesjy5UNPD2FwXR+v6SNp7Nm/JE2CUb9l+bKh5YeBrwArZ70aSZp99i9JY2/Uc8he03chktQH+5ekSTDSOWRJlib5qyQPJXkwyQeTLO27OEk6WPYvSZNg1JP63wtsAk4ElgD/qxuTpHFn/5I09kYNZIur6r1V9XD386fA4h7rkqTZYv+SNPZGDWRfT3JRkgXdz0XAN/osTJJmif1L0tgbNZD9JvBK4GvAbuAVgCfKSpoE9i9JY2/Uy178F2BNVf1fgCSLgKsYNDpJGmf2L0ljb9Q9ZM/Z28wAquofgDNGWbE7RHBHkg93zxcluTHJfd3jMUNzr0iyPcm9Sc5/Ir+IJO3DAfcvSZorowayQ6YFp0WMvnftMmDb0PPLgc1VtRzY3D0nyWnAauB04ALg6iQLRnwPSdqXg+lfkjQnRm1K/w34myTXMbjlyCuBdY+3Unetn3/Zzf0P3fBK4CXd8gbgk8B/7Mavrao9wI4k2xncAPhzI9YoSTM5oP4lSXNp1Cv1X5NkC4Mb8gZ4eVXdM8Kqfwj8HnDU0NjxVbW72+7uJMd140uAzw/N29WNSdIBO4j+JUlzZuTd9l0DG7mJJflXwENVdVuSl4yyykxvO8N21wJrAU4++eRRy5H0JPZE+5ckzbVRzyE7EOcAv5LkK8C1wEuT/DnwYJITALrHh7r5u4CThtZfCjwwfaNVtb6qVlTVisWLvbajJEmafL0Fsqq6oqqWVtUyBifr/3VVXcTgFiZrumlrgOu75U3A6iSHJzkFWA7c0ld9kiRJ46LFN43eCmxMcjFwP3AhQFVtTbKRwWGFh4FLquqRBvVJkiTNqTkJZFX1SQbfpqSqvgGcu4956/DbT5Ik6Ummz3PIJGmieWFrSXPFQCZJ++aFrSXNCQOZJM1g6MLW7x4aXsnggtZ0j6uGxq+tqj1VtQPYe2FrSRqJgUySZvaHDC5s/aOhsUdd2BoYvrD1zqF5Xtha0hNiIJOkaYYvbD3qKjOMPebC1t221ybZkmTL1NTUAdcoaX7xBrt6Qu5/y7NblzBWTn7TXa1LUD/2Xtj6l4GnAEcPX9i6u+3bE76wNQwubg2sB1ixYsWMoU3Sk497yCRpGi9sLWmuuYdMkkbnha0l9cJAJkn74YWtJc0FD1lKkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNLWxdgCRpZmf+7jWtSxgrt73tN1qXIPXGPWSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDXWWyBLclKSTyTZlmRrksu68UVJbkxyX/d4zNA6VyTZnuTeJOf3VZskSdI46XMP2cPAb1fVzwDPBy5JchpwObC5qpYDm7vndK+tBk4HLgCuTrKgx/okSZLGQm+BrKp2V9Xt3fJ3gG3AEmAlsKGbtgFY1S2vBK6tqj1VtQPYDpzdV32SJEnjYk7OIUuyDDgDuBk4vqp2wyC0Acd105YAO4dW29WNSZIkzWu9B7IkRwIfBN5QVd/e39QZxmqG7a1NsiXJlqmpqdkqU5L+P8+BlTTXeg1kSQ5lEMbeV1Uf6oYfTHJC9/oJwEPd+C7gpKHVlwIPTN9mVa2vqhVVtWLx4sX9FS/pycxzYCXNqT6/ZRngPcC2qnr70EubgDXd8hrg+qHx1UkOT3IKsBy4pa/6JGlfPAdW0lxb2OO2zwFeDdyV5M5u7D8BbwU2JrkYuB+4EKCqtibZCNzD4L/TS6rqkR7rk6THtb9zYJMMnwP7+aHVPAdW0hPSWyCrqs8w83lhAOfuY511wLq+apKkJ2L6ObCDHf8zT51h7DHnwHbbXAusBTj55JNno0xJ84BX6pekGfRxDix4HqykmRnIJGkaz4GVNNf6PIdMkiaV58BKmlMGMkmaxnNgJc01D1lKkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjBjJJkqTGDGSSJEmNGcgkSZIaM5BJkiQ1ZiCTJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxgxkkiRJjRnIJEmSGjOQSZIkNWYgkyRJasxAJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJkhozkEmSJDVmIJMkSWrMQCZJktSYgUySJKkxA5kkSVJjC1sXID3ZnfPOc1qXMFY+e+lnW5cgSXPOQCZJkg6I/1A+2sH8Qzl2hyyTXJDk3iTbk1zeuh5JGpX9S9KBGqtAlmQB8MfALwGnAa9KclrbqiTp8dm/JB2MsQpkwNnA9qr6clX9ALgWWNm4Jkkahf1L0gEbt0C2BNg59HxXNyZJ487+JemAjdtJ/ZlhrB41IVkLrO2efjfJvb1XNTuOBb7esoBctabl28+25p8nAG+e6U92Yo3FZ5rXP+5n+pNzUccBeNz+BRPbw8bjb8MeNrvsX7PuYPrXuAWyXcBJQ8+XAg8MT6iq9cD6uSxqNiTZUlUrWtcxX/h5zj4/04P2uP0LJrOH+bcx+/xMZ9d8+DzH7ZDlrcDyJKckOQxYDWxqXJMkjcL+JemAjdUesqp6OMnrgI8BC4A/qaqtjcuSpMdl/5J0MMYqkAFU1UeAj7SuowcTdYhiAvh5zj4/04Nk/9IT4Gc6uyb+80zVY845lSRJ0hwat3PIJEmSnnQMZD3zViqzK8mfJHkoyd2ta5kPkpyU5BNJtiXZmuSy1jVpfNi/Zpf9a/bNpx7mIcsedbdS+VvgFxh8Jf5W4FVVdU/TwiZYkhcD3wWuqapnta5n0iU5ATihqm5PchRwG7DKv1HZv2af/Wv2zace5h6yfnkrlVlWVTcB/9C6jvmiqnZX1e3d8neAbXh1eQ3Yv2aZ/Wv2zaceZiDrl7dS0cRIsgw4A7i5cSkaD/YvTZRJ72EGsn6NdCsVqbUkRwIfBN5QVd9uXY/Ggv1LE2M+9DADWb9GupWK1FKSQxk0svdV1Yda16OxYf/SRJgvPcxA1i9vpaKxliTAe4BtVfX21vVorNi/NPbmUw8zkPWoqh4G9t5KZRuw0VupHJwk7wc+B5yaZFeSi1vXNOHOAV4NvDTJnd3PL7cuSu3Zv2af/asX86aHedkLSZKkxtxDJkmS1JiBTJIkqTEDmSRJUmMGMkmSpMYMZJIkSY0ZyCRJGkGSZUnubl2H5icDmWZFkiuT/M4BrPeWJOf1UdMTdaC/gyTtlWRBg/dcONfvqdlnIFNTVfWmqvp46zokaUQLk2xI8sUk1yU5IslXkrwpyWeAC5O8KsldSe5O8gcASV6Z5O3d8mVJvtwtP6NbjyRvTXJPt+2rurHFST6Y5Nbu55xu/Mok65PcAFzT4oPQ7DKQ6YAl+c9J7k3yceDUbuwZST6a5LYkn07yzCRP7xrWId2cI5LsTHJokj9N8opu/Kwkf5PkC0luSXJUkgVJ3tY1oi8mee1+6nlJkk8l2Zjkb7vm9uvdtu5K8oxu3suS3JzkjiQfT3L8DNv6rST/J8lTk1zUbePOJP+jxX/AksbGqcD6qnoO8G3g33fj/1RVPwvcBPwB8FLgecBZSVZ14y/q5r4I+EaSJcDPAp9Osgj4VeD0btv/tZv7DuC/V9VZwL8G3j1Uy5nAyqr6N338oppbBjIdkCRnMri33RnAy4GzupfWA5dW1ZnA7wBXV9W3gC8AP9fNeRnwsar64dD2DgM+AFxWVc8FzgP+EbgY+FbXjM4CfivJKfsp7bnAZcCzGdxO46er6mwGTezSbs5ngOdX1RnAtcDvTfvdXtfVuApYBvwacE5VPQ94BPj1kT4kSfPRzqr6bLf85wwCFQz6Fwz61Ceraqq7/dT7gBdX1deAI5McxeCm7X8BvJhBOPs0g3D3T8C7k7wc+H63vfOAP0pyJ4N7iR7dbQNgU1X9Y0+/p+aYx511oF4E/FVVfR8gySbgKcALgb8c3O8VgMO7xw8wCDafYBDkrp62vVOB3VV1K0BVfbvb7i8Cz9m7Fw14OrAc2LGPum6tqt3dun8H3NCN3wX8fLe8FPhAkhOAw6Zt69XALmBVVf0wybkM/gu9tfudngo8tN9PRtJ8Nv1+g3uff697DPv2OeA1wL0MQthvAi8AfruqHk5yNnAugx75OgZ72Q4BXjA9eHX96Hto3nAPmQ7G9MZ0CPDNqnre0M/PdK9tAn6p2y1/JvDX09bNDNvbO37p0PZOqaobZpi3156h5R8NPf8RP/4H5J3AH1XVs4HXMgiSe93NYK/Y0qH33zD0/qdW1ZX7eX9J89vJSV7QLb+KwR73YTcDP5fk2O70hlcBn+peu4nBkYObgDsY/JO4p6q+leRI4OlV9RHgDQwOd8Lgn8rX7d14kr3jmmcMZDpQNwG/2p1jdRSDQ3zfB3YkuRAgA88FqKrvArcwOB/iw1X1yLTtfQk4MclZ3bpHdd8c+hjw75Ic2o3/dJKnHWTtTwe+2i2vmfbaHQxC2qYkJwKbgVckOa57/0VJfvIg31/S5NoGrEnyRWAR8K7hF7s99FcwOBrwBeD2qrq+e/nTDA5X3tT1wJ38ONAdBXy42+6ngDd2468HVnTn0N4D/NvefjM15SFLHZCquj3JB4A7gb9n0GhgcH7Vu5L8PnAog3O0vtC99gHgL4GXzLC9HyT5NeCdSZ7K4Pyx8xic+7UMuD2DffRTDM7tOhhXMjis+lXg88Cjzkmrqs9kcPmL/w38AvD7wA3dlxJ+CFzS/c6SnkSq6ivAaTO8tGzavL9gcI7Y9PX/jqFDmlX1i0PLu4GzZ1jn6wxO95g+fuXIhWsipGqmo0SSJEmaKx6ylCRJasxDlpo4SZ4N/Nm04T1V9S9a1CNJ0sHykKUkSVJjHrKUJElqzEAmSZLUmIFMkiSpMQOZJElSYwYySZKkxv4f82iaopbCogQAAAAASUVORK5CYII=","text/plain":["<Figure size 720x360 with 2 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["fig, ax = plt.subplots(1, 2, figsize=(10,5))\n","\n","sns.countplot(x=\"device_make\", data=df, ax=ax[0])\n","sns.countplot(x=\"browser\", data=df, ax=ax[1])\n","\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"Sk28cWC-B8nT"},"source":["**Checking the data distribution of the `device_make`, `browser`, and `platform_os` columns based on each experiment group.**"]},{"cell_type":"code","execution_count":27,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":606},"execution":{"iopub.execute_input":"2022-10-19T09:30:23.311237Z","iopub.status.busy":"2022-10-19T09:30:23.310710Z","iopub.status.idle":"2022-10-19T09:30:24.127737Z","shell.execute_reply":"2022-10-19T09:30:24.126386Z","shell.execute_reply.started":"2022-10-19T09:30:23.311185Z"},"executionInfo":{"elapsed":576,"status":"ok","timestamp":1679405190749,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"HizO_8PTB8nT","outputId":"66c9e30f-881f-46ff-e74a-173473a622c5","trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1440x576 with 3 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["fig, ax = plt.subplots(1,3,figsize=(20,8))\n","\n","sns.kdeplot(x='device_make', hue='experiment', data=df, ax=ax[0])\n","sns.kdeplot(x='browser', hue='experiment', data=df, ax=ax[1])\n","sns.kdeplot(x='platform_os', hue='experiment', data=df, ax=ax[2])\n","\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"KoRHZBIhB8nT"},"source":["**Checking the remaining data count for each observation group.**"]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1679405190749,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"X9TjT3IqUvU7","outputId":"59c6b40a-f0f1-485e-85f1-c52ef218bf2a"},"outputs":[{"data":{"text/plain":["exposed    657\n","control    586\n","Name: experiment, dtype: int64"]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["df['experiment'].value_counts()"]},{"cell_type":"code","execution_count":29,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":350},"execution":{"iopub.execute_input":"2022-10-19T09:30:24.207998Z","iopub.status.busy":"2022-10-19T09:30:24.207353Z","iopub.status.idle":"2022-10-19T09:30:24.444165Z","shell.execute_reply":"2022-10-19T09:30:24.442963Z","shell.execute_reply.started":"2022-10-19T09:30:24.207931Z"},"executionInfo":{"elapsed":468,"status":"ok","timestamp":1679405191215,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"0GtIN2BtB8nT","outputId":"2e99159a-7f26-4acc-ee1b-999f35835796","trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.countplot(x='experiment', data=df)\n","plt.title('Number of Observations per Group')\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["**Grouped Count of Ad Success by Experiment.**"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":13,"status":"ok","timestamp":1679405191215,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"iWv3STBPCuDo","outputId":"b8fb5aa2-1029-499e-f1f2-240c1bf81e13"},"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>if_ad_success</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2</th>\n","      <td>exposed</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>exposed</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>exposed</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>control</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>control</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>8059</th>\n","      <td>exposed</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>8063</th>\n","      <td>exposed</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>8064</th>\n","      <td>control</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8069</th>\n","      <td>control</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>8071</th>\n","      <td>exposed</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1243 rows × 2 columns</p>\n","</div>"],"text/plain":["     experiment if_ad_success\n","2       exposed             0\n","16      exposed             1\n","20      exposed             0\n","23      control             1\n","27      control             0\n","...         ...           ...\n","8059    exposed             1\n","8063    exposed             1\n","8064    control             0\n","8069    control             0\n","8071    exposed             0\n","\n","[1243 rows x 2 columns]"]},"execution_count":30,"metadata":{},"output_type":"execute_result"}],"source":["df.groupby(\"experiment\")[\"if_ad_success\"]\n","df[[\"experiment\", \"if_ad_success\"]]"]},{"cell_type":"markdown","metadata":{},"source":["**Group Data Segmentation :** Creating Control and Exposed Dataframes."]},{"cell_type":"code","execution_count":31,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"execution":{"iopub.execute_input":"2022-10-19T09:30:24.786036Z","iopub.status.busy":"2022-10-19T09:30:24.785641Z","iopub.status.idle":"2022-10-19T09:30:24.870847Z","shell.execute_reply":"2022-10-19T09:30:24.869899Z","shell.execute_reply.started":"2022-10-19T09:30:24.786003Z"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1679405191216,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"HZtRQc4oB8nU","outputId":"8c3cb7eb-f98e-40b5-8f0e-726488e733f4","trusted":true},"outputs":[],"source":["df_control = df[df.experiment =='control']\n","df_exposed = df[df.experiment =='exposed']"]},{"cell_type":"code","execution_count":32,"metadata":{},"outputs":[],"source":["conversion_rates = df.groupby('experiment')['if_ad_success']"]},{"cell_type":"code","execution_count":33,"metadata":{},"outputs":[],"source":["std_p = lambda x: np.std(x, ddof=0)   \n","std_err_p = lambda x: stats.sem(x, ddof=0)   "]},{"cell_type":"code","execution_count":34,"metadata":{},"outputs":[{"data":{"text/html":["<style type=\"text/css\">\n","</style>\n","<table id=\"T_d1b03_\">\n","  <thead>\n","    <tr>\n","      <th class=\"blank level0\" >&nbsp;</th>\n","      <th class=\"col_heading level0 col0\" >success_rate</th>\n","      <th class=\"col_heading level0 col1\" >std_deviation</th>\n","      <th class=\"col_heading level0 col2\" >std_error</th>\n","    </tr>\n","    <tr>\n","      <th class=\"index_name level0\" >experiment</th>\n","      <th class=\"blank col0\" >&nbsp;</th>\n","      <th class=\"blank col1\" >&nbsp;</th>\n","      <th class=\"blank col2\" >&nbsp;</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th id=\"T_d1b03_level0_row0\" class=\"row_heading level0 row0\" >control</th>\n","      <td id=\"T_d1b03_row0_col0\" class=\"data row0 col0\" >0.4505</td>\n","      <td id=\"T_d1b03_row0_col1\" class=\"data row0 col1\" >0.4975</td>\n","      <td id=\"T_d1b03_row0_col2\" class=\"data row0 col2\" >0.0206</td>\n","    </tr>\n","    <tr>\n","      <th id=\"T_d1b03_level0_row1\" class=\"row_heading level0 row1\" >exposed</th>\n","      <td id=\"T_d1b03_row1_col0\" class=\"data row1 col0\" >0.4688</td>\n","      <td id=\"T_d1b03_row1_col1\" class=\"data row1 col1\" >0.4990</td>\n","      <td id=\"T_d1b03_row1_col2\" class=\"data row1 col2\" >0.0195</td>\n","    </tr>\n","  </tbody>\n","</table>\n"],"text/plain":["<pandas.io.formats.style.Styler at 0x25f65e88e50>"]},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["conversion_rates = conversion_rates.agg([np.mean, std_p, std_err_p])\n","conversion_rates.columns = [\"success_rate\", \"std_deviation\", \"std_error\"]\n","\n","conversion_rates.style.format(\"{:.4f}\")"]},{"cell_type":"code","execution_count":35,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"execution":{"iopub.execute_input":"2022-10-19T09:44:25.375982Z","iopub.status.busy":"2022-10-19T09:44:25.375410Z","iopub.status.idle":"2022-10-19T09:44:25.609057Z","shell.execute_reply":"2022-10-19T09:44:25.607776Z","shell.execute_reply.started":"2022-10-19T09:44:25.375935Z"},"executionInfo":{"elapsed":1103,"status":"ok","timestamp":1679405192309,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"tMtFjcbNB8nU","outputId":"3ea8e984-10f3-477b-c0d1-01083cdc2f58","trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["fig, ax = plt.subplots()\n","\n","conversion_rates[\"success_rate\"].plot.bar()\n","\n","ax.bar_label(ax.containers[0], fmt=\"%.2f\", label_type=\"edge\", padding=2)\n","plt.title(\"Conversion Rate for Each Group\")\n","\n","plt.xticks(rotation=0)\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"bI7szEWjB8nU"},"source":["### **Conclusion**\n","\n","* The control and exposed groups accurately represent the population, as evidenced by the nearly identical distributions of `'device_make'`, `'browser'` and `'platform_os'` for both outcomes. \n","\n","* This is a positive sign, indicating that the division of the population between the control and exposed groups has been done in a way that both groups represent the total population.\n","\n","* The success rate (conversion rate) of the control group aligns with expectations at 45%. However, the success rate of the exposed group is below the expected 50%, coming in at 47%. This falls short of the initial expectation.\n"]},{"cell_type":"markdown","metadata":{"id":"TZ-2rohmB8nU"},"source":["### **4. Testing the hypothesis**\n"]},{"cell_type":"markdown","metadata":{},"source":["#### **Determining Sample Size.**\n","\n","Since the company aims for an improvement from 45% to 50%, we can use this as the effect size to determine the sample size. \n","\n","The goal is to ensure that the selected sample adequately represents the population, with a power of 0.8, alpha of 0.05, and a ratio of 1."]},{"cell_type":"code","execution_count":36,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2022-10-19T09:30:31.633782Z","iopub.status.busy":"2022-10-19T09:30:31.631764Z","iopub.status.idle":"2022-10-19T09:30:31.652481Z","shell.execute_reply":"2022-10-19T09:30:31.650947Z","shell.execute_reply.started":"2022-10-19T09:30:31.633699Z"},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1679405192310,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"TiCXpLEJB8nU","outputId":"d167ef4b-451e-491b-dbc3-f704e20d8c35","trusted":true},"outputs":[],"source":["effect_size = sms.proportion_effectsize(0.45, 0.50)\n","req_n = np.ceil(sms.NormalIndPower().solve_power(effect_size, power=0.8, alpha=0.05, ratio=1))"]},{"cell_type":"markdown","metadata":{},"source":["**Print the required sample size and the total remaining samples in the dataset.**"]},{"cell_type":"code","execution_count":37,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Required data count for each group : 1565\n","Total data count in the dataset : 1243\n"]}],"source":["print(f\"Required data count for each group : {int(req_n)}\")\n","print(f\"Total data count in the dataset : {df.shape[0]}\")"]},{"cell_type":"markdown","metadata":{"id":"XPWzrFFxB8nU"},"source":["After removing non-answers from the dataframe, the total data count in the dataset is significantly below the required data count for each group. \n","\n","Despite the data shortage, hypothesis testing will still be conducted to determine whether there is a significant difference in the success rate of ads between the two groups or otherwise."]},{"cell_type":"markdown","metadata":{"id":"ajalDCAhB8nU"},"source":["Testing the hypothesis involves calculating the **p-value**. \n","\n","- If the p-value is less than alpha=0.05, we cannot reject the null hypothesis. \n","\n","- OTOH, if the p-value is greater than 0.05, we can confidently reject the null hypothesis.\n"]},{"cell_type":"markdown","metadata":{},"source":["**Calculating the Success Rate for Each Group.**"]},{"cell_type":"code","execution_count":38,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{"iopub.execute_input":"2022-10-19T09:30:31.964786Z","iopub.status.busy":"2022-10-19T09:30:31.964256Z","iopub.status.idle":"2022-10-19T09:30:31.977676Z","shell.execute_reply":"2022-10-19T09:30:31.975933Z","shell.execute_reply.started":"2022-10-19T09:30:31.964737Z"},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1679405192310,"user":{"displayName":"Yoga Fatwanto","userId":"02503104669371919202"},"user_tz":-420},"id":"9zOT4xbzB8nU","outputId":"bb8bc24b-ca49-4c66-9731-ff8503bab098","trusted":true},"outputs":[],"source":["ad_success_count = [df_control.if_ad_success.sum(), df_exposed.if_ad_success.sum()]"]},{"cell_type":"markdown","metadata":{},"source":["**Calculating the Data Count in Each Group.**"]},{"cell_type":"code","execution_count":39,"metadata":{},"outputs":[],"source":["obsv_count = [df_control.if_ad_success.count(), df_exposed.if_ad_success.count()]"]},{"cell_type":"markdown","metadata":{},"source":["**Conducting Z-Test for Proportions.**"]},{"cell_type":"code","execution_count":40,"metadata":{},"outputs":[],"source":["z_stat, p_val = proportions_ztest(ad_success_count, nobs=obsv_count)"]},{"cell_type":"markdown","metadata":{},"source":["**Calculating Using 95% Confidence Intervals.**"]},{"cell_type":"code","execution_count":41,"metadata":{},"outputs":[],"source":["(l_ci_controlled, l_ci_exposed), (u_ci_controlled, u_ci_exposed) = proportion_confint(ad_success_count, nobs=obsv_count, alpha=0.05)"]},{"cell_type":"code","execution_count":42,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["z-statistic : -0.65\n","p-value : 0.5185\n"]}],"source":["print(f'z-statistic : {z_stat:.2f}')\n","print('p-value : {}'.format(p_val.round(4)))"]},{"cell_type":"code","execution_count":43,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["95% CI Value for the Controlled Group: [0.4102, 0.4908]\n","95% CI Value for the Exposed Group: [0.4306, 0.507]\n"]}],"source":["print(f\"95% CI Value for the Controlled Group: [{l_ci_controlled.round(4)}, {u_ci_controlled.round(4)}]\")\n","print(f\"95% CI Value for the Exposed Group: [{l_ci_exposed.round(4)}, {u_ci_exposed.round(4)}]\")"]},{"cell_type":"markdown","metadata":{"id":"AYFaDWspB8nU"},"source":["### **5. Conclusion**\n","\n","* The obtained p-value for 'ad_success' (0.5185) is significantly above the specified threshold (confidence level 95%, alpha = 0.05), and therefore, *****we cannot reject the 'Null Hypothesis' (Hₒ: p = pₒ), which states that there is no significant difference between the two groups.*****\n","\n","* After removing non-answers *(both 'yes' and 'no' columns == 0)* from the dataset, the remaining data count is only 1243 (losing **6834** data observations or **84.61%** of the initial dataset). The substantial loss of data leads to a lack of observations to ensure the detection of significant differences.\n","\n","* The target to increase the ad success rate to 50% has not been achieved using the new design strategy.\n","\n","* These findings suggest that there is no strong business justification to advocate for the implementation of the new ad design over the old one, as it will not yield additional benefits."]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.13"}},"nbformat":4,"nbformat_minor":0}