Code/Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries.\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset\n",
    "\n",
    "df = pd.read_csv('techsavvy_ab_test_data.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>group</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>clicked_add_to_cart</th>\n",
       "      <th>session_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>7/1/2024 0:00</td>\n",
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       "      <td>7/1/2024 0:01</td>\n",
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       "      <td>7/1/2024 0:03</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>A</td>\n",
       "      <td>7/1/2024 0:04</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   user_id group      timestamp  clicked_add_to_cart  session_duration\n",
       "0        1     A  7/1/2024 0:00                    0                66\n",
       "1        2     B  7/1/2024 0:01                    0                54\n",
       "2        3     A  7/1/2024 0:02                    0                41\n",
       "3        4     A  7/1/2024 0:03                    0                51\n",
       "4        5     A  7/1/2024 0:04                    0                44"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the first few rows of the dataframe to ensure it's loaded correctly\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>group</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>clicked_add_to_cart</th>\n",
       "      <th>session_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>9996</td>\n",
       "      <td>B</td>\n",
       "      <td>7/7/2024 22:35</td>\n",
       "      <td>0</td>\n",
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       "      <th>9996</th>\n",
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       "      <th>9998</th>\n",
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       "      <td>A</td>\n",
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       "      <td>64</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "      user_id group       timestamp  clicked_add_to_cart  session_duration\n",
       "9995     9996     B  7/7/2024 22:35                    0                45\n",
       "9996     9997     A  7/7/2024 22:36                    0                45\n",
       "9997     9998     B  7/7/2024 22:37                    0                55\n",
       "9998     9999     B  7/7/2024 22:38                    0                67\n",
       "9999    10000     A  7/7/2024 22:39                    0                64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the last few rows of the dataframe to ensure it's loaded correctly\n",
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conversion Rate A: 0.10\n",
      "Conversion Rate B: 0.12\n"
     ]
    }
   ],
   "source": [
    "# 1. Calculate Conversion Rates.\n",
    "\n",
    "# A/B Testing: Comparing two versions of a webpage to determine which one performs better.\n",
    "# Group A: Control group with the original version.\n",
    "# Group B: Treatment group with the modified version.\n",
    "\n",
    "\n",
    "# Conversion Rate: The percentage of users who click the \"Add to Cart\" button\n",
    "\n",
    "conversion_A = df[df['group'] == 'A']['clicked_add_to_cart'].mean()\n",
    "conversion_B = df[df['group'] == 'B']['clicked_add_to_cart'].mean()\n",
    "\n",
    "print(f\"Conversion Rate A: {conversion_A:.2f}\")\n",
    "print(f\"Conversion Rate B: {conversion_B:.2f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T-Statistic: -2.41\n",
      "P-Value (t-test): 0.0159\n"
     ]
    }
   ],
   "source": [
    "# 2. Perform Statistical Tests.\n",
    "\n",
    "# T-Test: A statistical test to determine if there are significant differences between the means of two groups.\n",
    "# Null Hypothesis: There is no difference in conversion rates between Group A and Group B.\n",
    "# Alternative Hypothesis: There is a difference in conversion rates between Group A and Group B.\n",
    "\n",
    "# Perform t-test\n",
    "t_stat, p_value_ttest = stats.ttest_ind(\n",
    "    df[df['group'] == 'A']['clicked_add_to_cart'],\n",
    "    df[df['group'] == 'B']['clicked_add_to_cart']\n",
    ")\n",
    "\n",
    "print(f\"T-Statistic: {t_stat:.2f}\")\n",
    "print(f\"P-Value (t-test): {p_value_ttest:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi-Square Statistic: 5.66\n",
      "P-Value (chi-square): 0.0173\n"
     ]
    }
   ],
   "source": [
    "# Perform chi-square test\n",
    "\n",
    "# Chi-Square Test: A statistical test to determine if there is a significant association between two categorical variables.\n",
    "# Null Hypothesis: There is no association between the group and the clicked \"Add to Cart\" button.\n",
    "# Alternative Hypothesis: There is an association between the group and the clicked \"Add to Cart\" button.\n",
    "\n",
    "contingency_table = pd.crosstab(df['group'], df['clicked_add_to_cart'])\n",
    "chi2_stat, p_value_chi2, _, _ = stats.chi2_contingency(contingency_table)\n",
    "\n",
    "print(f\"Chi-Square Statistic: {chi2_stat:.2f}\")\n",
    "print(f\"P-Value (chi-square): {p_value_chi2:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_40517/3551270136.py:11: FutureWarning: \n",
      "\n",
      "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
      "\n",
      "  sns.barplot(x='group', y='clicked_add_to_cart', data=df, ci=None)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. Visualize the Findings.\n",
    "\n",
    "# Visualization\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Bar plot of conversion rates by group.\n",
    "# Conversion Rate: The percentage of users who click the \"Add to Cart\" button.\n",
    "\n",
    "# Conversion rates\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.barplot(x='group', y='clicked_add_to_cart', data=df, ci=None)\n",
    "plt.title('Conversion Rate by Group')\n",
    "plt.xlabel('Group')\n",
    "plt.ylabel('Conversion Rate')\n",
    "\n",
    "# Box plot of session durations by group.\n",
    "# Session Duration: The length of time a user spends on the product page.\n",
    "\n",
    "# Session durations\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(x='group', y='session_duration', data=df)\n",
    "plt.title('Session Duration by Group')\n",
    "plt.xlabel('Group')\n",
    "plt.ylabel('Session Duration (seconds)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reject the null hypothesis. The button color change has a significant effect on user engagement.\n"
     ]
    }
   ],
   "source": [
    "# 4. Draw Conclusions.\n",
    "\n",
    "# Conclusion\n",
    "\n",
    "# Alpha: The significance level (typically set to 0.05).\n",
    "# P-Value: The probability that the observed results are due to chance.\n",
    "\n",
    "alpha = 0.05\n",
    "if p_value_ttest < alpha and p_value_chi2 < alpha:\n",
    "    print(\"Reject the null hypothesis. The button color change has a significant effect on user engagement.\")\n",
    "else:\n",
    "    print(\"Fail to reject the null hypothesis. The button color change does not have a significant effect on user engagement.\")\n"
   ]
  }
 ],
 "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": "3.10.13"
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 },
 "nbformat": 4,
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}