ab_testing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b24334e2-327b-4a41-aa93-3242240958a7",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "accd78a7-dc0c-4c9f-bf75-6c1104a731cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.stats.proportion import proportions_ztest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b453978e-e6cf-41a4-8054-6d599498e614",
   "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>userid</th>\n",
       "      <th>version</th>\n",
       "      <th>sum_gamerounds</th>\n",
       "      <th>retention_1</th>\n",
       "      <th>retention_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>116</td>\n",
       "      <td>gate_30</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>337</td>\n",
       "      <td>gate_30</td>\n",
       "      <td>38</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>377</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>165</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>483</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>488</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>179</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userid  version  sum_gamerounds  retention_1  retention_7\n",
       "0     116  gate_30               3        False        False\n",
       "1     337  gate_30              38         True        False\n",
       "2     377  gate_40             165         True        False\n",
       "3     483  gate_40               1        False        False\n",
       "4     488  gate_40             179         True         True"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"cookie_cats.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2ff09038-ca3c-4f1f-9fe0-4bacb4ed3e78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 90189 entries, 0 to 90188\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   userid          90189 non-null  int64 \n",
      " 1   version         90189 non-null  object\n",
      " 2   sum_gamerounds  90189 non-null  int64 \n",
      " 3   retention_1     90189 non-null  bool  \n",
      " 4   retention_7     90189 non-null  bool  \n",
      "dtypes: bool(2), int64(2), object(1)\n",
      "memory usage: 2.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "424957bd-c11b-439b-b6ce-e4c127432d09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userid            0\n",
       "version           0\n",
       "sum_gamerounds    0\n",
       "retention_1       0\n",
       "retention_7       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a75a162e-714e-4564-94e6-980b5989361b",
   "metadata": {},
   "source": [
    "## Define Player Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a71ef62f-28d1-44fe-ac77-76a0d6d70a3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold_new = df['sum_gamerounds'].quantile(0.25)\n",
    "threshold_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "245c6eac-dbe9-4847-b293-761892d052b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['player_experience'] = df['sum_gamerounds'].apply(lambda x: 'New' if x <= threshold_new else 'Experienced')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fd1d5ba1-b5cd-4fb7-b9a8-91ca82d163ae",
   "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>userid</th>\n",
       "      <th>version</th>\n",
       "      <th>sum_gamerounds</th>\n",
       "      <th>retention_1</th>\n",
       "      <th>retention_7</th>\n",
       "      <th>player_experience</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>116</td>\n",
       "      <td>gate_30</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>New</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>337</td>\n",
       "      <td>gate_30</td>\n",
       "      <td>38</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Experienced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>377</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>165</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Experienced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>483</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>New</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>488</td>\n",
       "      <td>gate_40</td>\n",
       "      <td>179</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>Experienced</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userid  version  sum_gamerounds  retention_1  retention_7 player_experience\n",
       "0     116  gate_30               3        False        False               New\n",
       "1     337  gate_30              38         True        False       Experienced\n",
       "2     377  gate_40             165         True        False       Experienced\n",
       "3     483  gate_40               1        False        False               New\n",
       "4     488  gate_40             179         True         True       Experienced"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e96586e0-23ec-4d6d-a530-99e56bc5ec10",
   "metadata": {},
   "source": [
    "## Split the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "286f1293-b2d5-4b22-abdc-db3e75d090ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_players = df[df['player_experience']=='New']\n",
    "experienced_players = df[df['player_experience']=='Experienced']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2344857a-c46a-4921-80e3-9ac8f79a291b",
   "metadata": {},
   "source": [
    "## Perform A/B Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65092485-4d91-48a3-b34f-b9e1d1732d13",
   "metadata": {},
   "source": [
    "### New Players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d0f181ca-eb2c-479e-b70d-87d63211b710",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New Players (Day 1) Z-Test: p-value = 0.03680478088374052 (Significant difference between gate_30 and gate_40)\n",
      "New Players (Day 7) Z-Test: p-value = 0.518804091696032 (No significant difference between gate_30 and gate_40)\n"
     ]
    }
   ],
   "source": [
    "# Split new players into control and test groups\n",
    "new_control = new_players[new_players['version']=='gate_30']\n",
    "new_test = new_players[new_players['version']=='gate_40']\n",
    "\n",
    "# Calculate counts for retention\n",
    "new_count_1 = [new_control['retention_1'].sum(), new_test['retention_1'].sum()]\n",
    "new_count_7 = [new_control['retention_7'].sum(), new_test['retention_7'].sum()]\n",
    "\n",
    "# Calculate the total number of observations\n",
    "new_nobs = [len(new_control), len(new_test)]\n",
    "\n",
    "# Perform Z-Test for Proportions\n",
    "new_stat_1, new_pval_1 = proportions_ztest(new_count_1, new_nobs)   \n",
    "new_stat_7, new_pval_7 = proportions_ztest(new_count_7, new_nobs)\n",
    "\n",
    "# Interpret the P-Values\n",
    "alpha = 0.05\n",
    "if new_pval_1 < alpha:\n",
    "    print(f\"New Players (Day 1) Z-Test: p-value = {new_pval_1} (Significant difference between gate_30 and gate_40)\")\n",
    "else:\n",
    "    print(f\"New Players (Day 1) Z-Test: p-value = {new_pval_1} (No significant difference between gate_30 and gate_40)\")\n",
    "    \n",
    "if new_pval_7 < alpha:\n",
    "    print(f\"New Players (Day 7) Z-Test: p-value = {new_pval_7} (Significant difference between gate_30 and gate_40)\")\n",
    "else:\n",
    "    print(f\"New Players (Day 7) Z-Test: p-value = {new_pval_7} (No significant difference between gate_30 and gate_40)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58aeb58c-b363-40dd-ad1f-0704125b1549",
   "metadata": {},
   "source": [
    "### Experienced Players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "761362ce-4310-4702-93a0-465de6861161",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experienced Players (Day 1) Z-Test: p-value = 0.2428595397580815 (No significant difference between gate_30 and gate_40)\n",
      "Experienced Players (Day 7) Z-Test: p-value = 0.009414437422179548 (Significant difference between gate_30 and gate_40)\n"
     ]
    }
   ],
   "source": [
    "# Split experienced players into control and test groups\n",
    "exp_control = experienced_players[experienced_players['version']=='gate_30']\n",
    "exp_test = experienced_players[experienced_players['version']=='gate_40']\n",
    "\n",
    "# Calculate counts for retention\n",
    "exp_count_1 = [exp_control['retention_1'].sum(), exp_test['retention_1'].sum()]\n",
    "exp_count_7 = [exp_control['retention_7'].sum(), exp_test['retention_7'].sum()]\n",
    "\n",
    "# Calculate the total number of observations\n",
    "exp_nobs = [len(exp_control), len(exp_test)]\n",
    "\n",
    "# Perform Z-Test for Proportions\n",
    "exp_stat_1, exp_pval_1 = proportions_ztest(exp_count_1, exp_nobs)   \n",
    "exp_stat_7, exp_pval_7 = proportions_ztest(exp_count_7, exp_nobs)\n",
    "\n",
    "# Interpret the P-Values\n",
    "alpha = 0.05\n",
    "if exp_pval_1 < alpha:\n",
    "    print(f\"Experienced Players (Day 1) Z-Test: p-value = {exp_pval_1} (Significant difference between gate_30 and gate_40)\")\n",
    "else:\n",
    "    print(f\"Experienced Players (Day 1) Z-Test: p-value = {exp_pval_1} (No significant difference between gate_30 and gate_40)\")\n",
    "    \n",
    "if exp_pval_7 < alpha:\n",
    "    print(f\"Experienced Players (Day 7) Z-Test: p-value = {exp_pval_7} (Significant difference between gate_30 and gate_40)\")\n",
    "else:\n",
    "    print(f\"Experienced Players (Day 7) Z-Test: p-value = {exp_pval_7} (No significant difference between gate_30 and gate_40)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0aca09c-c5a2-424d-b64c-8703230bb858",
   "metadata": {},
   "source": [
    "## Calculate Effect Sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "48d373cf-25c3-4a46-864b-d54a5a8433e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New Players Retention Rates:\n",
      "         retention_1  retention_7\n",
      "version                          \n",
      "gate_30     0.064781     0.012193\n",
      "gate_40     0.071479     0.013111\n"
     ]
    }
   ],
   "source": [
    "# Retention rates for new players\n",
    "new_group_summary = new_players.groupby('version')[['retention_1', 'retention_7']].mean()\n",
    "print(\"New Players Retention Rates:\")\n",
    "print(new_group_summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "31373abf-55e7-4ce8-8106-6db8609e5561",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert boolean values to 1 (True) and 0 (False) for retention calculations\n",
    "df['retention_1'] = df['retention_1'].astype(int)\n",
    "\n",
    "# Calculate the summary\n",
    "new_group_summary = new_players.groupby('version')[['retention_1', 'retention_7']].mean()\n",
    "\n",
    "# Plot the data\n",
    "new_group_summary.plot(kind='bar', figsize=(8,6), title='New Players Retention Rates', color=['skyblue', 'orange'])   \n",
    "\n",
    "# Customize the plot\n",
    "plt.ylabel(\"Retention Rate\")\n",
    "plt.xlabel(\"Version\")\n",
    "plt.xticks(rotation=0)  # To keep the x-axis labels horizontal\n",
    "plt.legend(title=\"Retention Period\", labels=[\"Day 1\", \"Day 7\"])\n",
    "\n",
    "plt.savefig(\"New_Retention_Rates.png\", transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "fbccd858-8ae5-4181-a878-a2cd8ac66b55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Experienced Players Retention Rates:\n",
      "         retention_1  retention_7\n",
      "version                          \n",
      "gate_30     0.589787     0.255943\n",
      "gate_40     0.585293     0.247137\n"
     ]
    }
   ],
   "source": [
    "# Retention rates for experienced players\n",
    "exp_group_summary = experienced_players.groupby('version')[['retention_1', 'retention_7']].mean()\n",
    "print(\"\\nExperienced Players Retention Rates:\")\n",
    "print(exp_group_summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "72858d33-4220-4b30-ba29-f3f228838bcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert boolean values to 1 (True) and 0 (False) for retention calculations\n",
    "df['retention_1'] = df['retention_1'].astype(int)\n",
    "df['retention_7'] = df['retention_7'].astype(int)\n",
    "\n",
    "# Calculate the summary\n",
    "exp_group_summary = experienced_players.groupby('version')[['retention_1', 'retention_7']].mean()\n",
    "\n",
    "# Plot the data\n",
    "exp_group_summary.plot(kind='bar', figsize=(8,6), title='Experienced Players Retention Rates', color=['skyblue', 'orange']) \n",
    "\n",
    "# Customize the plot\n",
    "plt.ylabel(\"Retention Rate\")\n",
    "plt.xlabel(\"Version\")\n",
    "plt.xticks(rotation=0)  # To keep the x-axis labels horizontal\n",
    "plt.legend(title=\"Retention Period\", labels=[\"Day 1\", \"Day 7\"])\n",
    "\n",
    "plt.savefig(\"Experienced_Retention_Rates.png\", transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5ce9606c-b3ef-45fa-99da-7fde2a024aaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New Players Uplift (Day 1): 10.34%\n",
      "New Players Uplift (Day 7): 7.53%\n",
      "Experienced Players Uplift (Day 1): -0.76%\n",
      "Experienced Players Uplift (Day 7): -3.44%\n"
     ]
    }
   ],
   "source": [
    "# New players' uplift\n",
    "uplift_1_new = (new_test['retention_1'].mean() - new_control['retention_1'].mean()) / new_control['retention_1'].mean()\n",
    "uplift_7_new = (new_test['retention_7'].mean() - new_control['retention_7'].mean()) / new_control['retention_7'].mean()\n",
    "\n",
    "# Experienced players' uplift\n",
    "uplift_1_exp = (exp_test['retention_1'].mean() - exp_control['retention_1'].mean()) / exp_control['retention_1'].mean()\n",
    "uplift_7_exp = (exp_test['retention_7'].mean() - exp_control['retention_7'].mean()) / exp_control['retention_7'].mean()\n",
    "\n",
    "# Print uplift percentages\n",
    "print(f\"New Players Uplift (Day 1): {uplift_1_new * 100:.2f}%\")\n",
    "print(f\"New Players Uplift (Day 7): {uplift_7_new * 100:.2f}%\")\n",
    "print(f\"Experienced Players Uplift (Day 1): {uplift_1_exp * 100:.2f}%\")\n",
    "print(f\"Experienced Players Uplift (Day 7): {uplift_7_exp * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "81daf68c-ac01-46f3-9dbe-0c374f95b8a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "uplift_data = {\n",
    "    'Group': ['New Players', 'New Players', 'Experienced Players', 'Experienced Players'],\n",
    "    'Day': ['Day 1', 'Day 7', 'Day 1', 'Day 7'],\n",
    "    'Uplift (%)': [10.34, 7.53, -0.76, -3.44]\n",
    "}\n",
    "\n",
    "df_uplift = pd.DataFrame(uplift_data)\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.barplot(data=df_uplift, x='Day', y='Uplift (%)', hue='Group')\n",
    "plt.axhline(0, color='gray', linestyle='--')\n",
    "plt.title(\"Uplift Comparison\")\n",
    "plt.ylabel(\"Uplift (%)\")\n",
    "plt.xlabel(\"\")\n",
    "plt.savefig(\"uplift.png\", transparent=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14a76b4f-f930-4fb6-8fa4-2e51126ec5a5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_1",
   "language": "python",
   "name": "conda_1"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}