diff --git a/lab04/Word_Embeddings_in_DF.ipynb b/lab04/Word_Embeddings_in_DF.ipynb
new file mode 100644
index 0000000..8027d75
--- /dev/null
+++ b/lab04/Word_Embeddings_in_DF.ipynb
@@ -0,0 +1,369 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Lab: Word Embeddings in Digital Forensics\n",
+        "\n",
+        "**Objective**: Learn how word embeddings represent text as vectors and use them to analyze dark web forum posts in a digital forensics investigation. The goal is to identify the true alias of a cybercriminal, \"The Ghost,\" from three candidates—\"ShadowTrader,\" \"DataReaper,\" or \"CrypticVendor\"—by uncovering semantic patterns in their posts.\n",
+        "\n",
+        "**Background Story**:  \n",
+        "It’s 2025, and you’re Analyst Quinn, a member of the Cyber Threat Task Force. A hacker known only as \"The Ghost\" has been wreaking havoc, selling stolen data—credit cards, passwords, and corporate secrets—on a dark web forum. The Task Force has intercepted three anonymous posts believed to be authored by The Ghost. Forum users mention different aliases—\"ShadowTrader (e.g., trading),\"  \"DataReaper (e.g., harvesting),\"  \"CrypticVendor (e..g, a vendor of hidden wares),\" —but only one is their true identity. Your mission: use word embeddings to analyze these posts and determine which alias most likely belongs to The Ghost, cutting through the noise to reveal their real handle.\n",
+        "\n",
+        "**Dataset**:  \n",
+        "Three dark web forum posts attributed to The Ghost:  \n",
+        "1. *\"Selling fresh (credit) card dumps, cheap prices, fast delivery.\"*  \n",
+        "2. *\"Got a haul of (login) credentials—banks, emails, you name it. DM for deals.\"*  \n",
+        "3. *\"Hacked a corporate (database)—juicy secrets for sale, secure payments only.\"*  \n",
+        "\n",
+        "**Goal**:  \n",
+        "Analyze the posts to extract key term, marked in \"()\", explore their semantic relationships, and predict The Ghost’s true alias—\"ShadowTrader,\" \"DataReaper,\" or \"CrypticVendor\"—based on their consistent language patterns."
+      ],
+      "metadata": {
+        "id": "WJOx7fI4tbu5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8QmsVWtglDMy",
+        "outputId": "3d52d6c8-8e67-4326-e7f9-078175609120"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Gensim version: 4.3.2\n",
+            "Scipy version: 1.10.1\n",
+            "Forensic toolkit ready, Agent Riley!\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Install compatible versions: gensim 4.3.2 and scipy 1.10.1\n",
+        "!pip install gensim==4.3.2 scipy==1.10.1 numpy matplotlib -q\n",
+        "\n",
+        "# Import libraries for embedding analysis and visualization\n",
+        "import gensim.downloader as api  # Access pre-trained embeddings\n",
+        "import numpy as np              # Handle vector math\n",
+        "import matplotlib.pyplot as plt # Visualize patterns\n",
+        "\n",
+        "# Verify installations\n",
+        "import gensim\n",
+        "import scipy\n",
+        "print(f\"Gensim version: {gensim.__version__}\")\n",
+        "print(f\"Scipy version: {scipy.__version__}\")\n",
+        "print(\"Forensic toolkit ready, Agent Riley!\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Load pre-trained Word2Vec model (Google News, 300D vectors)\n",
+        "model = api.load(\"word2vec-google-news-300\")\n",
+        "\n",
+        "# Test with a key term from Post 1: \"credit\"\n",
+        "word = \"credit\"\n",
+        "vector = model[word]\n",
+        "print(f\"Vector for '{word}' (first 5 dimensions): {vector[:5]}\")\n",
+        "print(f\"Vector length: {len(vector)}\")\n",
+        "\n",
+        "# Context: \"credit\" from \"credit card dumps\" is our first clue to The Ghost’s true alias"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uihL0xZElPit",
+        "outputId": "69a26003-2cfc-426a-ec12-542ff2bfa76d"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[==================================================] 100.0% 1662.8/1662.8MB downloaded\n",
+            "Vector for 'credit' (first 5 dimensions): [-0.01501465  0.19335938  0.01483154  0.07373047  0.24902344]\n",
+            "Vector length: 300\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Find words similar to \"credit\" to explore The Ghost’s focus\n",
+        "similar_words = model.most_similar(\"credit\", topn=5)\n",
+        "print(\"\\nWords similar to 'credit':\")\n",
+        "for word, similarity in similar_words:\n",
+        "    print(f\"{word}: {similarity:.3f}\")\n",
+        "\n",
+        "# Context: Do these terms (e.g., \"login\", \"database\") align with one alias over the others?"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FokP8-8Im8KX",
+        "outputId": "9db79c8b-a6c9-4645-ec8d-d51aae73d94c"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Words similar to 'credit':\n",
+            "Credit: 0.689\n",
+            "loan: 0.556\n",
+            "loans: 0.549\n",
+            "lending: 0.524\n",
+            "mortgage: 0.507\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Classic Word Embedding Example\n",
+        "# Demonstrate how embeddings capture relationships with \"king - man + woman ≈ queen\"\n",
+        "classic_result = model.most_similar(positive=[\"king\", \"woman\"], negative=[\"man\"], topn=1)\n",
+        "print(\"\\nClassic Example: king - man + woman = ?\")\n",
+        "print(f\"Result: {classic_result[0][0]} (similarity: {classic_result[0][1]:.3f})\")\n",
+        "print(\"This shows embeddings understand gender relationships—let’s visualize the relationships!\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NqwiuQVfnIiT",
+        "outputId": "a6768621-c3d8-4aa3-f027-40db985a8890"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Classic Example: king - man + woman = ?\n",
+            "Result: queen (similarity: 0.712)\n",
+            "This shows embeddings understand gender relationships—let’s visualize the relationships!\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Visulize classic Word Embedding Example in a figure\n",
+        "# Demonstrate how embeddings capture relationships with \"king - man + woman ≈ queen\"\n",
+        "classic_words = [\"king\", \"man\", \"woman\", \"queen\"]\n",
+        "classic_vectors = [model[word] for word in classic_words]\n",
+        "\n",
+        "# Compute the analogy vector explicitly: king - man + woman\n",
+        "analogy_vector = model[\"king\"] - model[\"man\"] + model[\"woman\"]\n",
+        "\n",
+        "# Perform the analogy to get the closest word (should be \"queen\")\n",
+        "classic_result = model.most_similar(positive=[\"king\", \"woman\"], negative=[\"man\"], topn=1)\n",
+        "print(\"\\nClassic Example: king - man + woman = ?\")\n",
+        "print(f\"Result: {classic_result[0][0]} (similarity: {classic_result[0][1]:.3f})\")\n",
+        "\n",
+        "# Reduce to 2D using PCA for visualization (include analogy vector)\n",
+        "from sklearn.decomposition import PCA\n",
+        "pca = PCA(n_components=2)\n",
+        "all_vectors = classic_vectors + [analogy_vector]  # Add analogy vector\n",
+        "reduced_vectors = pca.fit_transform(all_vectors)\n",
+        "\n",
+        "# Plot the classic embeddings with analogy highlighted\n",
+        "plt.figure(figsize=(10, 8))\n",
+        "# Plot the four words in blue\n",
+        "for i, word in enumerate(classic_words):\n",
+        "    plt.scatter(reduced_vectors[i, 0], reduced_vectors[i, 1], c=\"blue\", label=\"Words\" if i == 0 else \"\")\n",
+        "    plt.annotate(word, (reduced_vectors[i, 0], reduced_vectors[i, 1]), xytext=(5, 5), textcoords=\"offset points\")\n",
+        "# Plot the analogy vector in red\n",
+        "plt.scatter(reduced_vectors[4, 0], reduced_vectors[4, 1], c=\"red\", label=\"Analogy (king - man + woman)\")\n",
+        "plt.annotate(\"analogy\", (reduced_vectors[4, 0], reduced_vectors[4, 1]), xytext=(5, 5), textcoords=\"offset points\", c=\"red\")\n",
+        "# Draw an arrow from \"king\" to the analogy point\n",
+        "plt.arrow(reduced_vectors[0, 0], reduced_vectors[0, 1],\n",
+        "          reduced_vectors[4, 0] - reduced_vectors[0, 0],\n",
+        "          reduced_vectors[4, 1] - reduced_vectors[0, 1],\n",
+        "          color=\"red\", alpha=0.5, head_width=0.1, label=\"Shift\")\n",
+        "# Draw a dashed line from analogy to \"queen\" to show the intended result\n",
+        "queen_idx = classic_words.index(\"queen\")\n",
+        "plt.plot([reduced_vectors[4, 0], reduced_vectors[queen_idx, 0]],\n",
+        "         [reduced_vectors[4, 1], reduced_vectors[queen_idx, 1]],\n",
+        "         \"g--\", label=\"To Queen\", alpha=0.7)\n",
+        "plt.title(\"Classic Embedding Example: King - Man + Woman ≈ Queen\")\n",
+        "plt.xlabel(\"PCA Component 1\")\n",
+        "plt.ylabel(\"PCA Component 2\")\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "print(\"The red analogy point shifts from 'king' and should be near 'queen'—the green dashed line shows this connection!\")\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 787
+        },
+        "id": "s44gIukwtHT-",
+        "outputId": "259f2024-31bf-4c28-f9e6-d8f91d07310a"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Classic Example: king - man + woman = ?\n",
+            "Result: queen (similarity: 0.712)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x800 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The red analogy point shifts from 'king' and should be near 'queen'—the green dashed line shows this connection!\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Select key terms from the posts and alias-related words\n",
+        "words = [\"credit\", \"login\", \"database\", \"secrets\", \"payment\", \"sale\"]\n",
+        "vectors = [model[word] for word in words]\n",
+        "\n",
+        "# Reduce to 2D using PCA\n",
+        "from sklearn.decomposition import PCA\n",
+        "pca = PCA(n_components=2)\n",
+        "reduced_vectors = pca.fit_transform(vectors)\n",
+        "\n",
+        "# Plot the terms\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "for i, word in enumerate(words):\n",
+        "    plt.scatter(reduced_vectors[i, 0], reduced_vectors[i, 1])\n",
+        "    plt.annotate(word, (reduced_vectors[i, 0], reduced_vectors[i, 1]))\n",
+        "plt.title(\"The Ghost’s Terms in 2D Embedding Space\")\n",
+        "plt.xlabel(\"PCA Component 1\")\n",
+        "plt.ylabel(\"PCA Component 2\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "# Context: Clusters might hint at The Ghost’s alias—e.g., \"sale\" near \"credit\" for \"ShadowTrader\""
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "wN4_C1PTp4Fj",
+        "outputId": "4872597b-2523-4899-cb3b-97ee93571f8c"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Compare key post terms to alias-related terms\n",
+        "post_terms = [\"credit\", \"login\", \"database\"]  # From Posts 1, 2, 3\n",
+        "aliases = [\"trader\", \"reaper\", \"vendor\"]     # Simplified from ShadowTrader, DataReaper, CrypticVendor\n",
+        "\n",
+        "# Calculate average similarity between post terms and each alias term\n",
+        "for alias in aliases:\n",
+        "    similarities = [model.similarity(post_term, alias) for post_term in post_terms]\n",
+        "    avg_similarity = np.mean(similarities)\n",
+        "    print(f\"Average similarity of post terms to '{alias}': {avg_similarity:.3f}\")\n",
+        "\n",
+        "# Context: Higher similarity to \"trader,\" \"reaper,\" or \"vendor\" suggests The Ghost’s alias"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RU1WcTTM52Bs",
+        "outputId": "f2a9d8cb-678a-4ee5-a560-b79a20eac15e"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Average similarity of post terms to 'trader': 0.049\n",
+            "Average similarity of post terms to 'reaper': 0.032\n",
+            "Average similarity of post terms to 'vendor': 0.130\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Conclusion\n",
+        "\n",
+        "*   CrypticVendor is the hacker's alias."
+      ],
+      "metadata": {
+        "id": "EKammd8SO6Vd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "s0zUJibb57bW"
+      },
+      "execution_count": 7,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file