Experimental Design.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.stats.proportion as sp\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AB Testing / Experimental Design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_abtest = pd.read_csv('ab_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>group</th>\n",
       "      <th>landing_page</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>851104</td>\n",
       "      <td>2017-01-21 22:11:49</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>804228</td>\n",
       "      <td>2017-01-12 08:01:45</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>661590</td>\n",
       "      <td>2017-01-11 16:55:06</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>853541</td>\n",
       "      <td>2017-01-08 18:28:03</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>864975</td>\n",
       "      <td>2017-01-21 01:52:26</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294473</th>\n",
       "      <td>751197</td>\n",
       "      <td>2017-01-03 22:28:39</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294474</th>\n",
       "      <td>945152</td>\n",
       "      <td>2017-01-12 00:51:57</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294475</th>\n",
       "      <td>734608</td>\n",
       "      <td>2017-01-22 11:45:03</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294476</th>\n",
       "      <td>697314</td>\n",
       "      <td>2017-01-15 01:20:29</td>\n",
       "      <td>control</td>\n",
       "      <td>old_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294477</th>\n",
       "      <td>715931</td>\n",
       "      <td>2017-01-16 12:40:24</td>\n",
       "      <td>treatment</td>\n",
       "      <td>new_page</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294478 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id            timestamp      group landing_page  converted\n",
       "0        851104  2017-01-21 22:11:49    control     old_page          0\n",
       "1        804228  2017-01-12 08:01:45    control     old_page          0\n",
       "2        661590  2017-01-11 16:55:06  treatment     new_page          0\n",
       "3        853541  2017-01-08 18:28:03  treatment     new_page          0\n",
       "4        864975  2017-01-21 01:52:26    control     old_page          1\n",
       "...         ...                  ...        ...          ...        ...\n",
       "294473   751197  2017-01-03 22:28:39    control     old_page          0\n",
       "294474   945152  2017-01-12 00:51:57    control     old_page          0\n",
       "294475   734608  2017-01-22 11:45:03    control     old_page          0\n",
       "294476   697314  2017-01-15 01:20:29    control     old_page          0\n",
       "294477   715931  2017-01-16 12:40:24  treatment     new_page          0\n",
       "\n",
       "[294478 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_abtest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Separate Control and Treatment Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "control = df_abtest[df_abtest['group']=='control']\n",
    "treatment = df_abtest[df_abtest['group']=='treatment']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Number of Success and Number of Observation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_success_control = control['converted'].sum()\n",
    "n_success_treatment = treatment['converted'].sum()\n",
    "\n",
    "n_obs_control = control['converted'].count()\n",
    "n_obs_treatment = treatment['converted'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "control_conversion_rate = n_success_control/n_obs_control\n",
    "treatment_conversion_rate = n_success_treatment/n_obs_treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conversion Rate from Control : 0.1204\n",
      "Conversion Rate from Treatment : 0.1189\n"
     ]
    }
   ],
   "source": [
    "print('Conversion Rate from Control : {0:0.4f}'.format(control_conversion_rate))\n",
    "print('Conversion Rate from Treatment : {0:0.4f}'.format(treatment_conversion_rate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.118, 0.1205)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = ['control','treatment']\n",
    "value = [control_conversion_rate,treatment_conversion_rate]\n",
    "plt.bar(x,value)\n",
    "\n",
    "plt.ylim(0.118,0.1205)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***is the difference significance?*** <br>\n",
    "\n",
    "## Do Hypothesis Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## making array\n",
    "success = np.array([n_success_control,n_success_treatment])\n",
    "obs = np.array([n_obs_control,n_obs_treatment])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "H0: existing design and new design **have same** conversion rate <br>\n",
    "H1: existing design and new design **do not have same** conversion rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats,pvalue = sp.proportions_ztest(success,obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P-Value : 0.21611613269757501\n",
      "Insufficient to reject H0\n"
     ]
    }
   ],
   "source": [
    "print('P-Value : '+ str(pvalue))\n",
    "if pvalue >= 0.05:\n",
    "    print('Insufficient to reject H0')\n",
    "else :\n",
    "    print('Sufficient to reject H0')"
   ]
  }
 ],
 "metadata": {
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}