diff --git a/backlog/CommunityEngagementAnalysis_survey_results_public_2018.ipynb b/backlog/CommunityEngagementAnalysis_survey_results_public_2018.ipynb
new file mode 100644
index 0000000..1dacb63
--- /dev/null
+++ b/backlog/CommunityEngagementAnalysis_survey_results_public_2018.ipynb
@@ -0,0 +1,396 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "kGS021mjjc2C"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load the dataset\n",
+        "df = pd.read_csv('/content/drive/MyDrive/GirlsScriptOpenSource/StockOverflow/survey_results_public_2018.csv')"
+      ],
+      "metadata": {
+        "id": "13VZHs0qjw9K",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c6e782f7-1abe-4c29-8b79-ba954026f11e"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-2-c242cc7acb8e>:2: DtypeWarning: Columns (8,12,13,14,15,16,50,51,52,53,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+            "  df = pd.read_csv('/content/drive/MyDrive/GirlsScriptOpenSource/StockOverflow/survey_results_public_2018.csv')\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Display the first few rows of the dataframe\n",
+        "print(df.head())"
+      ],
+      "metadata": {
+        "id": "RGkDcjpKjxAF",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "716fadd8-72ff-44e7-f421-3be8a45d4f27"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "   Respondent Hobby OpenSource         Country         Student  \\\n",
+            "0           1   Yes         No           Kenya              No   \n",
+            "1           3   Yes        Yes  United Kingdom              No   \n",
+            "2           4   Yes        Yes   United States              No   \n",
+            "3           5    No         No   United States              No   \n",
+            "4           7   Yes         No    South Africa  Yes, part-time   \n",
+            "\n",
+            "           Employment                                    FormalEducation  \\\n",
+            "0  Employed part-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+            "1  Employed full-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+            "2  Employed full-time                                   Associate degree   \n",
+            "3  Employed full-time           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+            "4  Employed full-time  Some college/university study without earning ...   \n",
+            "\n",
+            "                                      UndergradMajor  \\\n",
+            "0                          Mathematics or statistics   \n",
+            "1  A natural science (ex. biology, chemistry, phy...   \n",
+            "2  Computer science, computer engineering, or sof...   \n",
+            "3  Computer science, computer engineering, or sof...   \n",
+            "4  Computer science, computer engineering, or sof...   \n",
+            "\n",
+            "                CompanySize  \\\n",
+            "0        20 to 99 employees   \n",
+            "1  10,000 or more employees   \n",
+            "2        20 to 99 employees   \n",
+            "3      100 to 499 employees   \n",
+            "4  10,000 or more employees   \n",
+            "\n",
+            "                                             DevType  ...  \\\n",
+            "0                               Full-stack developer  ...   \n",
+            "1  Database administrator;DevOps specialist;Full-...  ...   \n",
+            "2           Engineering manager;Full-stack developer  ...   \n",
+            "3                               Full-stack developer  ...   \n",
+            "4  Data or business analyst;Desktop or enterprise...  ...   \n",
+            "\n",
+            "                     Exercise Gender         SexualOrientation  \\\n",
+            "0        3 - 4 times per week   Male  Straight or heterosexual   \n",
+            "1   Daily or almost every day   Male  Straight or heterosexual   \n",
+            "2                         NaN    NaN                       NaN   \n",
+            "3  I don't typically exercise   Male  Straight or heterosexual   \n",
+            "4        3 - 4 times per week   Male  Straight or heterosexual   \n",
+            "\n",
+            "                                    EducationParents  \\\n",
+            "0           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+            "1           Bachelor’s degree (BA, BS, B.Eng., etc.)   \n",
+            "2                                                NaN   \n",
+            "3  Some college/university study without earning ...   \n",
+            "4  Some college/university study without earning ...   \n",
+            "\n",
+            "                  RaceEthnicity                Age Dependents  MilitaryUS  \\\n",
+            "0   Black or of African descent  25 - 34 years old        Yes         NaN   \n",
+            "1  White or of European descent  35 - 44 years old        Yes         NaN   \n",
+            "2                           NaN                NaN        NaN         NaN   \n",
+            "3  White or of European descent  35 - 44 years old         No          No   \n",
+            "4  White or of European descent  18 - 24 years old        Yes         NaN   \n",
+            "\n",
+            "                          SurveyTooLong     SurveyEasy  \n",
+            "0  The survey was an appropriate length      Very easy  \n",
+            "1  The survey was an appropriate length  Somewhat easy  \n",
+            "2                                   NaN            NaN  \n",
+            "3  The survey was an appropriate length  Somewhat easy  \n",
+            "4  The survey was an appropriate length  Somewhat easy  \n",
+            "\n",
+            "[5 rows x 129 columns]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Select relevant columns for community engagement analysis\n",
+        "engagement_columns = ['StackOverflowVisit', 'StackOverflowParticipate', 'StackOverflowHasAccount',\n",
+        "                      'StackOverflowConsiderMember', 'StackOverflowJobs', 'StackOverflowDevStory',\n",
+        "                      'StackOverflowJobsRecommend']"
+      ],
+      "metadata": {
+        "id": "NmQ4dan5j2YB"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Filter the dataset with selected columns\n",
+        "engagement_df = df[engagement_columns]"
+      ],
+      "metadata": {
+        "id": "d3CryMonj2bA"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Display the first few rows of the filtered dataframe\n",
+        "print(engagement_df.head())"
+      ],
+      "metadata": {
+        "id": "RcD9Fl7kj2dh",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "974cf907-0b08-4c1c-959e-824713241d00"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                StackOverflowVisit  \\\n",
+            "0           Multiple times per day   \n",
+            "1  A few times per month or weekly   \n",
+            "2                              NaN   \n",
+            "3             A few times per week   \n",
+            "4            Daily or almost daily   \n",
+            "\n",
+            "                            StackOverflowParticipate StackOverflowHasAccount  \\\n",
+            "0  I have never participated in Q&A on Stack Over...                     Yes   \n",
+            "1                    A few times per month or weekly                     Yes   \n",
+            "2                                                NaN                     NaN   \n",
+            "3                    A few times per month or weekly                     Yes   \n",
+            "4                Less than once per month or monthly                     Yes   \n",
+            "\n",
+            "  StackOverflowConsiderMember  \\\n",
+            "0                         Yes   \n",
+            "1                         Yes   \n",
+            "2                         NaN   \n",
+            "3                         Yes   \n",
+            "4                         Yes   \n",
+            "\n",
+            "                                   StackOverflowJobs  \\\n",
+            "0  No, I knew that Stack Overflow had a jobs boar...   \n",
+            "1                                                Yes   \n",
+            "2                                                NaN   \n",
+            "3                                                Yes   \n",
+            "4  No, I knew that Stack Overflow had a jobs boar...   \n",
+            "\n",
+            "                        StackOverflowDevStory StackOverflowJobsRecommend  \n",
+            "0                                         Yes                        NaN  \n",
+            "1         No, I have one but it's out of date                          7  \n",
+            "2                                         NaN                        NaN  \n",
+            "3         No, I have one but it's out of date                          8  \n",
+            "4  No, I know what it is but I don't have one                        NaN  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Function to plot engagement metrics\n",
+        "def plot_engagement_metric(column, title):\n",
+        "    plt.figure(figsize=(10, 6))\n",
+        "    sns.countplot(y=engagement_df[column], order = engagement_df[column].value_counts().index)\n",
+        "    plt.title(title)\n",
+        "    plt.xlabel('Count')\n",
+        "    plt.ylabel('Response')\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "qAifqbnLj2gl"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot engagement metrics for StackOverflowVisit\n",
+        "plot_engagement_metric('StackOverflowVisit', 'Frequency of Stack Overflow Visits')"
+      ],
+      "metadata": {
+        "id": "dbiQ6P3NkBMe",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "outputId": "44c6df40-8b62-46dd-ee2c-f5bccc2cb7ee"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot engagement metrics for StackOverflowParticipate\n",
+        "plot_engagement_metric('StackOverflowParticipate', 'Frequency of Stack Overflow Participation')"
+      ],
+      "metadata": {
+        "id": "BvXgyMIIkBPm",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "outputId": "3642acec-6a50-4330-dfa9-88189e974dd5"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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ArIbwCQAAAAAAAFbzjw6fIiMj5erqmtVlIJNMJpOWL1+ermOHDRumKlWqWLWeZ3Hu3DmZTCYdPHgw1WM8PT01adKkv60mZExUVJRMJpNu3ryZ1aVk2PLly1W6dGnZ2tqqb9++/LcRAAAAQLaSpeFTaGiomjdvnpUl4DlILTiKjY1Vo0aN0tXGgAEDtHHjxudaV3oCo+dpz5496t69+zO3c+zYMbVu3Vpubm5ycHBQ2bJlNXToUN29ezfZsd988428vLyUM2dOeXp6auTIkWm2HRgYKFtbW+3Zs+eZ68ysK1euqGfPnipWrJgcHBzk7u6uwMBAbdu2zXxMRoJLa7tw4YI6d+6sIkWKyN7eXsWLF1efPn107dq1v62GHj16qGXLlrpw4cJT7zEAAAAA/NP8o0c+IeP++uuvv+1chmHo4cOHqe53d3eXg4NDutpycnJS/vz5n1dpWcLNzU25cuV6pjZ27typF198UX/99ZdWrVqlkydPatSoUYqMjFTDhg0t7u+5c+cUEhKi5s2bKyYmRkuWLFGJEiVSbfv8+fPavn27wsLCNHv27Geq81m0aNFCBw4c0Jw5c3Ty5EmtXLlSAQEBf2uYk16//PKLqlevrlOnTmnhwoU6ffq0ZsyYoY0bN6pWrVq6fv26Vc//4MEDxcXF6fLlywoMDFSRIkXk7Oxs1XMCAAAAwPOWLcKndevWydvbW05OTgoKClJsbKx53549e9SwYUMVKFBAefLkkb+/v/bv32/e365dO7Vp08aivQcPHqhAgQKaO3euJCkxMVFjxoxRiRIl5OjoKB8fH3333Xdp1uTp6anRo0erc+fOcnZ2VrFixfTVV19ZHHPhwgW1bt1arq6uypcvn5o1a6Zz585Jkn788UflzJkz2RSgPn36qF69eubXW7dulZ+fnxwdHeXh4aHevXvrzp07FnWMHDlSISEhcnFxSXXkTUBAgMLCwhQWFqY8efKoQIECGjJkiAzDMB8zb948Va9eXc7OznJ3d1e7du10+fJl8/6kaUtr1qxRtWrV5ODgoG+++UbDhw/XoUOHZDKZZDKZFBkZKSn56JXffvtNbdu2Vb58+ZQ7d25Vr15du3btkpR89FTSqLjhw4fLzc1NLi4uevvtty3Cl7Vr1+rll1+Wq6ur8ufPryZNmujMmTPm/UlBjK+vr0wmkwICAsz7Zs2aJW9vb+XMmVNeXl768ssvLfpr9+7d8vX1Vc6cOVW9enUdOHAgxX593JPT7kwmk2bNmqU33nhDuXLlUpkyZbRy5cpU328Yhrp06SJvb28tW7ZMNWvWVPHixdWqVSv973//044dOzRx4kSL9k0mkzp37qwSJUqoZs2aat++fartR0REqEmTJurZs6cWLlyoe/fuPfWali5dqgoVKsjBwUGenp4aP358smt+2ufgcTdv3tSWLVv06aef6pVXXlHx4sVVs2ZNDR48WK+//rq5TUl64403ZDKZzK/PnDmjZs2aqVChQnJyclKNGjW0YcMGi/bj4+M1aNAgeXh4yMHBQaVLl1Z4eHiKtdy9e1eNGjVSnTp1Up2K16tXL9nb2+vHH3+Uv7+/ihUrpkaNGmnDhg36/fff9eGHH0qS/u///k8vvvhisvf7+PhoxIgR5tdpPXdJI/UWL14sf39/5cyZU/PnzzeHTfXq1ZPJZFJUVFSKtU6fPl2lSpWSvb29ypUrp3nz5pn3DRgwQE2aNDG/njRpkkwmk9auXWveVrp0ac2aNSvFtgEAAADgWfzjw6e7d+9q3LhxmjdvnjZv3qzz589rwIAB5v1//vmnOnbsqK1bt2rnzp0qU6aMXnvtNf3555+SpODgYP3vf/9TXFyc+T3r1q3T3bt39cYbb0iSxowZo7lz52rGjBk6duyY+vXrp/bt2ys6OjrN2saPH28OJt555x317NlTJ06ckPQo4AoMDJSzs7O2bNmibdu2mcOzv/76S/Xr15erq6uWLl1qbi8hIUGLFy9WcHCwpEdftoOCgtSiRQsdPnxYixcv1tatWxUWFmZRx7hx4+Tj46MDBw5oyJAhqdY7Z84c2dnZaffu3Zo8ebImTJhg8WXzwYMHGjlypA4dOqTly5fr3LlzCg0NTdbOBx98oLFjxyomJkYNGzbUe++9pwoVKig2NlaxsbHJwj5JiouLk7+/v37//XetXLlShw4d0vvvv6/ExMRU6924caNiYmIUFRWlhQsXatmyZRo+fLh5/507d9S/f3/t3btXGzdulI2Njd544w1zm7t375YkbdiwQbGxsVq2bJkkaf78+Ro6dKhGjRqlmJgYjR49WkOGDNGcOXPMtTZp0kTly5fXvn37NGzYMItnLiOGDx+u1q1b6/Dhw3rttdcUHByc6miZgwcP6vjx4+rfv79sbCw/mj4+PmrQoIEWLlxo3la0aFFVr15dYWFhun//fpp1GIahiIgItW/fXl5eXipduvRTA9Z9+/apdevWeuutt3TkyBENGzZMQ4YMMYeLSdL6HDzJyclJTk5OWr58ueLj41M8JmlKYEREhGJjY82v4+Li9Nprr2njxo06cOCAgoKC1LRpU50/f9783pCQEC1cuFBTpkxRTEyMZs6cKScnp2TnuHnzpho2bKjExEStX78+xfWTrl+/rnXr1umdd96Ro6OjxT53d3cFBwdr8eLFMgxDwcHB2r17t0X4eezYMR0+fFjt2rWT9PTnLskHH3ygPn36KCYmRq+88oq5L5cuXarY2FjVrl07Wa3ff/+9+vTpo/fee09Hjx5Vjx491KlTJ23atEmS5O/vr61btyohIUGSFB0drQIFCpiDrN9//11nzpyxCGiTxMfH6/bt2xZ/AAAAAJARdlldwNM8ePBAM2bMUKlSpSRJYWFhFiMJHh8lJElfffWVXF1dFR0drSZNmigwMFC5c+fW999/rw4dOkiSFixYoNdff13Ozs6Kj4/X6NGjtWHDBtWqVUuSVLJkSW3dulUzZ86Uv79/qrW99tpreueddyRJgwYN0sSJE7Vp0yaVK1dOixcvVmJiombNmiWTySTp0ZdpV1dXRUVF6dVXX9Vbb72lBQsWqEuXLpIehS03b95UixYtJD0KxYKDg9W3b19JUpkyZTRlyhT5+/tr+vTpypkzp7kP3nvvvaf2pYeHhyZOnCiTyaRy5crpyJEjmjhxorp16yZJ6ty5s/nYkiVLasqUKapRo4bi4uIsvsCPGDFCDRs2NL92cnKSnZ2d3N3dUz33ggULdOXKFe3Zs0f58uWT9GikRVrs7e01e/Zs5cqVSxUqVNCIESM0cOBAjRw5UjY2NuZ+SjJ79my5ubnp+PHjqlixotzc3CRJ+fPnt6jt448/1vjx4/Xmm29KejRC6vjx45o5c6Y6duyoBQsWKDExUeHh4cqZM6cqVKig3377TT179kyz3pSEhoaqbdu2kqTRo0drypQp2r17t4KCgpIde/LkSUmSt7d3im15e3tr69at5tfdunWTYRgqWbKkGjVqpBUrVsjFxUWS1LRpUxUvXlzTpk2T9CiAu3v3rgIDAyVJ7du3V3h4uPkzkZIJEyaofv365kCzbNmyOn78uD7//HOLUDKtz8GT7OzsFBkZqW7dumnGjBmqWrWq/P399dZbb6ly5cqSZL5vrq6uFvfNx8dHPj4+5tcjR47U999/r5UrVyosLEwnT57UkiVLtH79ejVo0EDSo+f4SX/88YfatGmjMmXKaMGCBbK3t0/x+k+dOiXDMNK8Hzdu3NCVK1dUoUIF+fj4aMGCBeb+mj9/vl588UXzc/605y5J3759zcdIMo/KypcvX6qfsXHjxik0NNR8H/r376+dO3dq3LhxeuWVV+Tn56c///xTBw4cULVq1bR582YNHDjQPDIxKipKRYsWTfEzOWbMGIvQFwAAAAAy6h8/8ilXrlzm4EmSChcubDEV7NKlS+rWrZvKlCmjPHnyyMXFRXFxcebREHZ2dmrdurXmz58v6dFomRUrVphHF50+fVp3795Vw4YNzaMynJycNHfuXItRDClJ+rIsPZoC5e7ubq7t0KFDOn36tJydnc1t5suXT/fv3ze3GxwcrKioKF28eFHSoy+rjRs3No/COHTokCIjIy3qCgwMVGJios6ePWs+d/Xq1dPVly+99JI5CJOkWrVq6dSpU+bREPv27VPTpk1VrFgxOTs7m4O3x0eWZOR8jzt48KB8fX3NwVN6+Pj4WKyhVKtWLcXFxenChQuSHoUDbdu2VcmSJeXi4mKenvVkvY+7c+eOzpw5oy5dulj06yeffGK+LzExMapcubI53Es6d2Y8/ozkzp1bLi4uFs9vSh6fCvmkpKDk+PHjioyMVGRkpKZPn65ixYopICDA3PbRo0fl5+dnft/s2bPVpk0b2dk9ypvbtm2rbdu2pfmMx8TEqE6dOhbb6tSpY/HMPHmNT34OUtKiRQtdvHhRK1euVFBQkKKiolS1atVkI6qeFBcXpwEDBsjb21uurq5ycnJSTEyM+X4fPHhQtra2aQbGktSwYUOVLl1aixcvTjV4elxa9+NxwcHBWrBggfk9CxcuNP93Jj3PXZLMfL5Su1cxMTGSHgV5Pj4+ioqK0pEjR2Rvb6/u3bvrwIEDiouLU3R0dKr9NnjwYN26dcv8l/T5AwAAAID0+sePfMqRI4fFa5PJZPFlsGPHjrp27ZomT56s4sWLy8HBQbVq1bJYGyg4OFj+/v66fPmy1q9fL0dHR/PIk6TpeKtWrVLRokUtzvW0xbJTqi1pyldcXJyqVatmDr0elzSyo0aNGipVqpQWLVqknj176vvvv7f4Ah4XF6cePXqod+/eydooVqyY+d+5c+dOs870uHPnjgIDAxUYGKj58+fLzc1N58+fV2BgYLJFzDNzvienLT0PSaN7vv76axUpUkSJiYmqWLFimouuJ93vr7/+OtkaPba2ts+9xrSekSeVKVNG0qMgwdfXN9n+mJgYlS1bVpJ0+PBhOTg4qHz58pL+f7hUp04dvf/++/rzzz/Nayh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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Analysis of users who visit Stack Overflow frequently and participate\n",
+        "frequent_visitors = engagement_df[engagement_df['StackOverflowVisit'] == 'Multiple times per day']\n",
+        "frequent_participants = frequent_visitors[frequent_visitors['StackOverflowParticipate'] == 'Yes']"
+      ],
+      "metadata": {
+        "id": "nmv-_DEvkBSf"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Display statistics for frequent participants\n",
+        "print(frequent_participants.describe())"
+      ],
+      "metadata": {
+        "id": "Y-IaGXp6kBVi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "53ea6c5e-b16f-482f-c5b7-d44248220e2a"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "       StackOverflowVisit StackOverflowParticipate StackOverflowHasAccount  \\\n",
+            "count                   0                        0                       0   \n",
+            "unique                  0                        0                       0   \n",
+            "top                   NaN                      NaN                     NaN   \n",
+            "freq                  NaN                      NaN                     NaN   \n",
+            "\n",
+            "       StackOverflowConsiderMember StackOverflowJobs StackOverflowDevStory  \\\n",
+            "count                            0                 0                     0   \n",
+            "unique                           0                 0                     0   \n",
+            "top                            NaN               NaN                   NaN   \n",
+            "freq                           NaN               NaN                   NaN   \n",
+            "\n",
+            "       StackOverflowJobsRecommend  \n",
+            "count                           0  \n",
+            "unique                          0  \n",
+            "top                           NaN  \n",
+            "freq                          NaN  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Plot the relationship between having a Stack Overflow account and participation\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "sns.countplot(x='StackOverflowHasAccount', hue='StackOverflowParticipate', data=engagement_df)\n",
+        "plt.title('Stack Overflow Account vs Participation')\n",
+        "plt.xlabel('Has Stack Overflow Account')\n",
+        "plt.ylabel('Count')\n",
+        "plt.legend(title='Participate')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "my-TlrSzkBZq",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "outputId": "9ca05d72-0644-4f0c-a3ae-fa15c343e981"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "JTZIGiBUkPUv"
+      },
+      "execution_count": 12,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file