{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## **Assignment #2: Classification, Regression, Clustering, Evaluation**"
      ],
      "metadata": {
        "id": "w84cR3AZIU0e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install ucimlrepo"
      ],
      "metadata": {
        "id": "738iKFKoTJ2G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3baa4ea-1ad3-425e-e8ba-e04a2d699035"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ucimlrepo\n",
            "  Downloading ucimlrepo-0.0.7-py3-none-any.whl.metadata (5.5 kB)\n",
            "Requirement already satisfied: pandas>=1.0.0 in /usr/local/lib/python3.12/dist-packages (from ucimlrepo) (2.2.2)\n",
            "Requirement already satisfied: certifi>=2020.12.5 in /usr/local/lib/python3.12/dist-packages (from ucimlrepo) (2025.11.12)\n",
            "Requirement already satisfied: numpy>=1.26.0 in /usr/local/lib/python3.12/dist-packages (from pandas>=1.0.0->ucimlrepo) (2.0.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas>=1.0.0->ucimlrepo) (2.9.0.post0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas>=1.0.0->ucimlrepo) (2025.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.12/dist-packages (from pandas>=1.0.0->ucimlrepo) (2025.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.12/dist-packages (from python-dateutil>=2.8.2->pandas>=1.0.0->ucimlrepo) (1.17.0)\n",
            "Downloading ucimlrepo-0.0.7-py3-none-any.whl (8.0 kB)\n",
            "Installing collected packages: ucimlrepo\n",
            "Successfully installed ucimlrepo-0.0.7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import ipywidgets as widgets\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.cluster import KMeans, AgglomerativeClustering, DBSCAN\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.metrics import silhouette_score, make_scorer, classification_report, accuracy_score\n",
        "from sklearn.model_selection import GridSearchCV, train_test_split\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from scipy.cluster.hierarchy import dendrogram, linkage"
      ],
      "metadata": {
        "id": "zVoe9Hq20Yc_"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from ucimlrepo import fetch_ucirepo\n",
        "\n",
        "# fetch dataset\n",
        "online_news_popularity = fetch_ucirepo(id=332)\n",
        "\n",
        "# data (as pandas dataframes)\n",
        "X = online_news_popularity.data.features\n",
        "y = online_news_popularity.data.targets\n",
        "\n",
        "# metadata\n",
        "print(online_news_popularity.metadata)\n",
        "\n",
        "# variable information\n",
        "print(online_news_popularity.variables)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PJpBGPfPTUll",
        "outputId": "bba1c155-74eb-465e-98e5-a2f72f5cfd04"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'uci_id': 332, 'name': 'Online News Popularity', 'repository_url': 'https://archive.ics.uci.edu/dataset/332/online+news+popularity', 'data_url': 'https://archive.ics.uci.edu/static/public/332/data.csv', 'abstract': 'This dataset summarizes a heterogeneous set of features about articles published by Mashable in a period of two years. The goal is to predict the number of shares in social networks (popularity).', 'area': 'Business', 'tasks': ['Classification', 'Regression'], 'characteristics': ['Multivariate'], 'num_instances': 39797, 'num_features': 58, 'feature_types': ['Integer', 'Real'], 'demographics': [], 'target_col': [' shares'], 'index_col': ['url'], 'has_missing_values': 'no', 'missing_values_symbol': None, 'year_of_dataset_creation': 2015, 'last_updated': 'Thu Feb 15 2024', 'dataset_doi': '10.24432/C5NS3V', 'creators': ['Kelwin Fernandes', 'Pedro Vinagre', 'Paulo Cortez', 'Pedro Sernadela'], 'intro_paper': {'ID': 390, 'type': 'NATIVE', 'title': 'A Proactive Intelligent Decision Support System for Predicting the Popularity of Online News', 'authors': 'Kelwin Fernandes, Pedro Vinagre, P. Cortez', 'venue': 'Portuguese Conference on Artificial Intelligence', 'year': 2015, 'journal': None, 'DOI': None, 'URL': 'https://www.semanticscholar.org/paper/A-Proactive-Intelligent-Decision-Support-System-for-Fernandes-Vinagre/ad7f3da7a5d6a1e18cc5a176f18f52687b912fea', 'sha': None, 'corpus': None, 'arxiv': None, 'mag': None, 'acl': None, 'pmid': None, 'pmcid': None}, 'additional_info': {'summary': '* The articles were published by Mashable (www.mashable.com) and their content as the rights to reproduce it belongs to them. Hence, this dataset does not share the original content but some statistics associated with it. The original content be publicly accessed and retrieved using the provided urls.\\r\\n* Acquisition date: January 8, 2015\\r\\n* The estimated relative performance values were estimated by the authors using a Random Forest classifier and a rolling windows as assessment method.  See their article for more details on how the relative performance values were set.', 'purpose': None, 'funded_by': None, 'instances_represent': None, 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': \"Number of Attributes: 61 (58 predictive attributes, 2 non-predictive, 1 goal field)\\r\\n\\r\\nAttribute Information:\\r\\n     0. url:                           URL of the article (non-predictive)\\r\\n     1. timedelta:                     Days between the article publication and the dataset acquisition (non-predictive)\\r\\n     2. n_tokens_title:                Number of words in the title\\r\\n     3. n_tokens_content:              Number of words in the content\\r\\n     4. n_unique_tokens:               Rate of unique words in the content\\r\\n     5. n_non_stop_words:              Rate of non-stop words in the content\\r\\n     6. n_non_stop_unique_tokens:      Rate of unique non-stop words in the content\\r\\n     7. num_hrefs:                     Number of links\\r\\n     8. num_self_hrefs:                Number of links to other articles published by Mashable\\r\\n     9. num_imgs:                      Number of images\\r\\n    10. num_videos:                    Number of videos\\r\\n    11. average_token_length:          Average length of the words in the content\\r\\n    12. num_keywords:                  Number of keywords in the metadata\\r\\n    13. data_channel_is_lifestyle:     Is data channel 'Lifestyle'?\\r\\n    14. data_channel_is_entertainment: Is data channel 'Entertainment'?\\r\\n    15. data_channel_is_bus:           Is data channel 'Business'?\\r\\n    16. data_channel_is_socmed:        Is data channel 'Social Media'?\\r\\n    17. data_channel_is_tech:          Is data channel 'Tech'?\\r\\n    18. data_channel_is_world:         Is data channel 'World'?\\r\\n    19. kw_min_min:                    Worst keyword (min. shares)\\r\\n    20. kw_max_min:                    Worst keyword (max. shares)\\r\\n    21. kw_avg_min:                    Worst keyword (avg. shares)\\r\\n    22. kw_min_max:                    Best keyword (min. shares)\\r\\n    23. kw_max_max:                    Best keyword (max. shares)\\r\\n    24. kw_avg_max:                    Best keyword (avg. shares)\\r\\n    25. kw_min_avg:                    Avg. keyword (min. shares)\\r\\n    26. kw_max_avg:                    Avg. keyword (max. shares)\\r\\n    27. kw_avg_avg:                    Avg. keyword (avg. shares)\\r\\n    28. self_reference_min_shares:     Min. shares of referenced articles in Mashable\\r\\n    29. self_reference_max_shares:     Max. shares of referenced articles in Mashable\\r\\n    30. self_reference_avg_sharess:    Avg. shares of referenced articles in Mashable\\r\\n    31. weekday_is_monday:             Was the article published on a Monday?\\r\\n    32. weekday_is_tuesday:            Was the article published on a Tuesday?\\r\\n    33. weekday_is_wednesday:          Was the article published on a Wednesday?\\r\\n    34. weekday_is_thursday:           Was the article published on a Thursday?\\r\\n    35. weekday_is_friday:             Was the article published on a Friday?\\r\\n    36. weekday_is_saturday:           Was the article published on a Saturday?\\r\\n    37. weekday_is_sunday:             Was the article published on a Sunday?\\r\\n    38. is_weekend:                    Was the article published on the weekend?\\r\\n    39. LDA_00:                        Closeness to LDA topic 0\\r\\n    40. LDA_01:                        Closeness to LDA topic 1\\r\\n    41. LDA_02:                        Closeness to LDA topic 2\\r\\n    42. LDA_03:                        Closeness to LDA topic 3\\r\\n    43. LDA_04:                        Closeness to LDA topic 4\\r\\n    44. global_subjectivity:           Text subjectivity\\r\\n    45. global_sentiment_polarity:     Text sentiment polarity\\r\\n    46. global_rate_positive_words:    Rate of positive words in the content\\r\\n    47. global_rate_negative_words:    Rate of negative words in the content\\r\\n    48. rate_positive_words:           Rate of positive words among non-neutral tokens\\r\\n    49. rate_negative_words:           Rate of negative words among non-neutral tokens\\r\\n    50. avg_positive_polarity:         Avg. polarity of positive words\\r\\n    51. min_positive_polarity:         Min. polarity of positive words\\r\\n    52. max_positive_polarity:         Max. polarity of positive words\\r\\n    53. avg_negative_polarity:         Avg. polarity of negative  words\\r\\n    54. min_negative_polarity:         Min. polarity of negative  words\\r\\n    55. max_negative_polarity:         Max. polarity of negative  words\\r\\n    56. title_subjectivity:            Title subjectivity\\r\\n    57. title_sentiment_polarity:      Title polarity\\r\\n    58. abs_title_subjectivity:        Absolute subjectivity level\\r\\n    59. abs_title_sentiment_polarity:  Absolute polarity level\\r\\n    60. shares:                        Number of shares (target)\", 'citation': None}}\n",
            "                             name     role         type demographic  \\\n",
            "0                             url       ID  Categorical        None   \n",
            "1                       timedelta    Other   Continuous        None   \n",
            "2                  n_tokens_title  Feature   Continuous        None   \n",
            "3                n_tokens_content  Feature   Continuous        None   \n",
            "4                 n_unique_tokens  Feature   Continuous        None   \n",
            "..                            ...      ...          ...         ...   \n",
            "56             title_subjectivity  Feature   Continuous        None   \n",
            "57       title_sentiment_polarity  Feature   Continuous        None   \n",
            "58         abs_title_subjectivity  Feature   Continuous        None   \n",
            "59   abs_title_sentiment_polarity  Feature   Continuous        None   \n",
            "60                         shares   Target      Integer        None   \n",
            "\n",
            "   description units missing_values  \n",
            "0         None  None             no  \n",
            "1         None  None             no  \n",
            "2         None  None             no  \n",
            "3         None  None             no  \n",
            "4         None  None             no  \n",
            "..         ...   ...            ...  \n",
            "56        None  None             no  \n",
            "57        None  None             no  \n",
            "58        None  None             no  \n",
            "59        None  None             no  \n",
            "60        None  None             no  \n",
            "\n",
            "[61 rows x 7 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Combine X (features) and y (target) into one DataFrame\n",
        "df = pd.concat([X, y], axis=1)\n",
        "\n",
        "# Check the shape to confirm (should be roughly 39k rows, 61 columns)\n",
        "print(f\"Dataset Shape: {df.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t3iGOIPbTjkg",
        "outputId": "51924f1e-e6cc-4d61-9833-40934854e7bb"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Shape: (39644, 59)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "X1PoOsdZUsYM",
        "outputId": "de43ecdf-a5ae-46e3-ea4c-ecd54ecea0b9"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   n_tokens_title  n_tokens_content  n_unique_tokens  n_non_stop_words  \\\n",
              "0            12.0             219.0         0.663594               1.0   \n",
              "1             9.0             255.0         0.604743               1.0   \n",
              "2             9.0             211.0         0.575130               1.0   \n",
              "3             9.0             531.0         0.503788               1.0   \n",
              "4            13.0            1072.0         0.415646               1.0   \n",
              "\n",
              "   n_non_stop_unique_tokens  num_hrefs  num_self_hrefs  num_imgs  num_videos  \\\n",
              "0                  0.815385        4.0             2.0       1.0         0.0   \n",
              "1                  0.791946        3.0             1.0       1.0         0.0   \n",
              "2                  0.663866        3.0             1.0       1.0         0.0   \n",
              "3                  0.665635        9.0             0.0       1.0         0.0   \n",
              "4                  0.540890       19.0            19.0      20.0         0.0   \n",
              "\n",
              "   average_token_length  ...  min_positive_polarity  max_positive_polarity  \\\n",
              "0              4.680365  ...               0.100000                    0.7   \n",
              "1              4.913725  ...               0.033333                    0.7   \n",
              "2              4.393365  ...               0.100000                    1.0   \n",
              "3              4.404896  ...               0.136364                    0.8   \n",
              "4              4.682836  ...               0.033333                    1.0   \n",
              "\n",
              "   avg_negative_polarity  min_negative_polarity  max_negative_polarity  \\\n",
              "0              -0.350000                 -0.600              -0.200000   \n",
              "1              -0.118750                 -0.125              -0.100000   \n",
              "2              -0.466667                 -0.800              -0.133333   \n",
              "3              -0.369697                 -0.600              -0.166667   \n",
              "4              -0.220192                 -0.500              -0.050000   \n",
              "\n",
              "   title_subjectivity  title_sentiment_polarity  abs_title_subjectivity  \\\n",
              "0            0.500000                 -0.187500                0.000000   \n",
              "1            0.000000                  0.000000                0.500000   \n",
              "2            0.000000                  0.000000                0.500000   \n",
              "3            0.000000                  0.000000                0.500000   \n",
              "4            0.454545                  0.136364                0.045455   \n",
              "\n",
              "   abs_title_sentiment_polarity  shares  \n",
              "0                      0.187500     593  \n",
              "1                      0.000000     711  \n",
              "2                      0.000000    1500  \n",
              "3                      0.000000    1200  \n",
              "4                      0.136364     505  \n",
              "\n",
              "[5 rows x 59 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4d48761f-cd3f-4543-898c-fa9350227990\" class=\"colab-df-container\">\n",
              "    <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>n_tokens_title</th>\n",
              "      <th>n_tokens_content</th>\n",
              "      <th>n_unique_tokens</th>\n",
              "      <th>n_non_stop_words</th>\n",
              "      <th>n_non_stop_unique_tokens</th>\n",
              "      <th>num_hrefs</th>\n",
              "      <th>num_self_hrefs</th>\n",
              "      <th>num_imgs</th>\n",
              "      <th>num_videos</th>\n",
              "      <th>average_token_length</th>\n",
              "      <th>...</th>\n",
              "      <th>min_positive_polarity</th>\n",
              "      <th>max_positive_polarity</th>\n",
              "      <th>avg_negative_polarity</th>\n",
              "      <th>min_negative_polarity</th>\n",
              "      <th>max_negative_polarity</th>\n",
              "      <th>title_subjectivity</th>\n",
              "      <th>title_sentiment_polarity</th>\n",
              "      <th>abs_title_subjectivity</th>\n",
              "      <th>abs_title_sentiment_polarity</th>\n",
              "      <th>shares</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12.0</td>\n",
              "      <td>219.0</td>\n",
              "      <td>0.663594</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.815385</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.680365</td>\n",
              "      <td>...</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.7</td>\n",
              "      <td>-0.350000</td>\n",
              "      <td>-0.600</td>\n",
              "      <td>-0.200000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>-0.187500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.187500</td>\n",
              "      <td>593</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.0</td>\n",
              "      <td>255.0</td>\n",
              "      <td>0.604743</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.791946</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.913725</td>\n",
              "      <td>...</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>0.7</td>\n",
              "      <td>-0.118750</td>\n",
              "      <td>-0.125</td>\n",
              "      <td>-0.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.0</td>\n",
              "      <td>211.0</td>\n",
              "      <td>0.575130</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.663866</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.393365</td>\n",
              "      <td>...</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.466667</td>\n",
              "      <td>-0.800</td>\n",
              "      <td>-0.133333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9.0</td>\n",
              "      <td>531.0</td>\n",
              "      <td>0.503788</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.665635</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.404896</td>\n",
              "      <td>...</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>0.8</td>\n",
              "      <td>-0.369697</td>\n",
              "      <td>-0.600</td>\n",
              "      <td>-0.166667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13.0</td>\n",
              "      <td>1072.0</td>\n",
              "      <td>0.415646</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.540890</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.682836</td>\n",
              "      <td>...</td>\n",
              "      <td>0.033333</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.220192</td>\n",
              "      <td>-0.500</td>\n",
              "      <td>-0.050000</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>0.045455</td>\n",
              "      <td>0.136364</td>\n",
              "      <td>505</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 59 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d48761f-cd3f-4543-898c-fa9350227990')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4d48761f-cd3f-4543-898c-fa9350227990 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4d48761f-cd3f-4543-898c-fa9350227990');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    <div id=\"df-e9889a08-3d08-475c-840d-f72ee46152a5\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e9889a08-3d08-475c-840d-f72ee46152a5')\"\n",
              "                title=\"Suggest charts\"\n",
              "                style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "      <script>\n",
              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "        }\n",
              "        (() => {\n",
              "          let quickchartButtonEl =\n",
              "            document.querySelector('#df-e9889a08-3d08-475c-840d-f72ee46152a5 button');\n",
              "          quickchartButtonEl.style.display =\n",
              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "        })();\n",
              "      </script>\n",
              "    </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**EDA**"
      ],
      "metadata": {
        "id": "ILjKniXdyToR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fix the hidden spaces in column names\n",
        "df.columns = df.columns.str.strip()\n",
        "\n",
        "# Verify it worked by printing the first few columns\n",
        "print(df.columns.tolist()[:5])\n",
        "\n",
        "# --- 1. CHECK FOR MISSING VALUES ---\n",
        "missing_values = df.isnull().sum().sum()\n",
        "print(f\"Total Missing Values: {missing_values}\")\n",
        "\n",
        "# --- 2. CHECK FOR DUPLICATES ---\n",
        "duplicates = df.duplicated().sum()\n",
        "print(f\"Total Duplicate Rows: {duplicates}\")\n",
        "\n",
        "# If (and only if) there were duplicates, we would drop them:\n",
        "# df = df.drop_duplicates()\n",
        "\n",
        "# --- 3. REMOVE LOGICAL ERRORS ---\n",
        "# Some articles have 0 words in the content (n_tokens_content = 0).\n",
        "# These are likely image-only posts or errors. Let's remove them to be rigorous.\n",
        "initial_rows = df.shape[0]\n",
        "df = df[df['n_tokens_content'] > 0]\n",
        "print(f\"Removed {initial_rows - df.shape[0]} empty articles. New shape: {df.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sxXdCTzHyVy_",
        "outputId": "34d538e7-9725-4913-cba2-b253ad94b850"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['n_tokens_title', 'n_tokens_content', 'n_unique_tokens', 'n_non_stop_words', 'n_non_stop_unique_tokens']\n",
            "Total Missing Values: 0\n",
            "Total Duplicate Rows: 0\n",
            "Removed 1181 empty articles. New shape: (38463, 59)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's look at the correlation of numerical features with 'shares'\n",
        "# We select a subset of interesting numeric columns to keep the chart readable\n",
        "numeric_cols = [\n",
        "    'n_tokens_content', 'num_hrefs', 'num_imgs', 'num_videos',\n",
        "    'global_subjectivity', 'global_sentiment_polarity', 'shares'\n",
        "]\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "# Calculate correlation matrix\n",
        "corr_matrix = df[numeric_cols].corr()\n",
        "\n",
        "# Plot Heatmap\n",
        "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
        "plt.title('Correlation Heatmap: Content Features vs. Shares')\n",
        "plt.show()\n",
        "\n",
        "# Insight Calculation\n",
        "print(\"Top 5 Features correlated with Shares:\")\n",
        "print(df.corrwith(df['shares']).abs().sort_values(ascending=False).head(5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 989
        },
        "id": "Wa4ELTVyyeSx",
        "outputId": "f9bba666-6350-4eec-d775-1fe3b3e2ba88"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top 5 Features correlated with Shares:\n",
            "shares                 1.000000\n",
            "kw_avg_avg             0.108515\n",
            "LDA_03                 0.081206\n",
            "kw_max_avg             0.063620\n",
            "global_subjectivity    0.061016\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see in this heatmap, the raw features have very weak linear correlations with our target variable, 'shares'. The strongest predictor here is 'Subjectivity' at only 0.06. This indicates that virality is complex and non-linear, which validates our decision to use advanced tree-based models and feature engineering rather than simple regression."
      ],
      "metadata": {
        "id": "T5TnsITr5b8w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "# --- 0. PRE-REQUISITE FIX: Ensure Data is Split ---\n",
        "# If SEED wasn't defined, define it now\n",
        "SEED = 42\n",
        "\n",
        "# Ensure 'log_shares' exists in the main dataframe\n",
        "if 'log_shares' not in df.columns:\n",
        "    df['log_shares'] = np.log1p(df['shares'])\n",
        "\n",
        "# Re-Split the data into Train and Validation\n",
        "train_end = int(len(df) * 0.70)\n",
        "val_end = int(len(df) * 0.85)\n",
        "\n",
        "train_df = df.iloc[:train_end].copy()\n",
        "val_df = df.iloc[train_end:val_end].copy()\n",
        "\n",
        "# Define X (Features) and y (Target)\n",
        "# We drop non-numeric/target columns\n",
        "drop_cols = ['url', 'timedelta', 'shares', 'log_shares', 'channel', 'day', 'is_popular']\n",
        "features = [c for c in df.columns if c not in drop_cols]\n",
        "\n",
        "X_train = train_df[features]\n",
        "X_val = val_df[features]\n",
        "\n",
        "print(\"✅ Data re-loaded. Now training models...\")\n",
        "\n",
        "# --- 1. SETUP DATA FOR REGRESSION ---\n",
        "y_train_reg = train_df['log_shares']\n",
        "y_val_reg = val_df['log_shares']\n",
        "\n",
        "# --- 2. TRAIN 3 MODELS ---\n",
        "# Model A: Linear Regression (Baseline)\n",
        "reg_lin = LinearRegression()\n",
        "reg_lin.fit(X_train, y_train_reg)\n",
        "\n",
        "# Model B: Random Forest\n",
        "reg_rf = RandomForestRegressor(n_estimators=100, max_depth=10, random_state=SEED, n_jobs=-1)\n",
        "reg_rf.fit(X_train, y_train_reg)\n",
        "\n",
        "# Model C: Gradient Boosting\n",
        "reg_gb = GradientBoostingRegressor(n_estimators=100, max_depth=5, random_state=SEED)\n",
        "reg_gb.fit(X_train, y_train_reg)\n",
        "\n",
        "# --- 3. EVALUATE & COMPARE NUMBERS ---\n",
        "models = [reg_lin, reg_rf, reg_gb]\n",
        "model_names = ['Linear Regression', 'Random Forest', 'Gradient Boosting']\n",
        "results = []\n",
        "\n",
        "for name, model in zip(model_names, models):\n",
        "    y_pred_log = model.predict(X_val)\n",
        "    # Convert back to real shares for readable errors\n",
        "    y_pred_real = np.expm1(y_pred_log)\n",
        "    y_val_real = np.expm1(y_val_reg)\n",
        "\n",
        "    rmse = np.sqrt(mean_squared_error(y_val_real, y_pred_real))\n",
        "    r2 = r2_score(y_val_real, y_pred_real)\n",
        "    results.append({'Model': name, 'RMSE (Lower is Better)': rmse, 'R2 Score': r2})\n",
        "\n",
        "# Display the Comparison Table\n",
        "reg_results_df = pd.DataFrame(results)\n",
        "print(\"\\n🏆 REGRESSION LEADERBOARD 🏆\")\n",
        "display(reg_results_df)\n",
        "\n",
        "# --- 4. VISUAL COMPARISON ---\n",
        "plt.figure(figsize=(10, 5))\n",
        "sns.barplot(x='RMSE (Lower is Better)', y='Model', data=reg_results_df, palette='viridis')\n",
        "plt.title('Regression Error Comparison (RMSE)')\n",
        "plt.xlabel('Average Error (Shares)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        },
        "id": "Fj_dCJBoKyu0",
        "outputId": "ac470a38-83eb-480a-c944-0e44c1d17f3a"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Data re-loaded. Now training models...\n",
            "\n",
            "🏆 REGRESSION LEADERBOARD 🏆\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "               Model  RMSE (Lower is Better)  R2 Score\n",
              "0  Linear Regression             6469.713772  0.002944\n",
              "1      Random Forest             6450.252306  0.008933\n",
              "2  Gradient Boosting             6444.840966  0.010595"
            ],
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              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>RMSE (Lower is Better)</th>\n",
              "      <th>R2 Score</th>\n",
              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>Linear Regression</td>\n",
              "      <td>6469.713772</td>\n",
              "      <td>0.002944</td>\n",
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              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
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              "      <td>0.008933</td>\n",
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              "    <tr>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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              "variable_name": "reg_results_df",
              "summary": "{\n  \"name\": \"reg_results_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Linear Regression\",\n          \"Random Forest\",\n          \"Gradient Boosting\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE (Lower is Better)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.081078829152407,\n        \"min\": 6444.840966258785,\n        \"max\": 6469.7137720636265,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6469.7137720636265,\n          6450.252305563667,\n          6444.840966258785\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R2 Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004024583654946181,\n        \"min\": 0.0029438743032524917,\n        \"max\": 0.010595499485046123,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0029438743032524917,\n          0.008933316909883526,\n          0.010595499485046123\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipython-input-169227723.py:73: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x='RMSE (Lower is Better)', y='Model', data=reg_results_df, palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Gradient Boosting** was chosen as the best regression model for this dataset."
      ],
      "metadata": {
        "id": "MIuwfgpfLmrO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "plt.scatter(y_val_real, y_pred_real, alpha=0.3, color='blue')\n",
        "\n",
        "# Draw a perfect prediction line (y=x)\n",
        "min_val = min(y_val_real.min(), y_pred_real.min())\n",
        "max_val = 50000 # Limit zoom because of outliers\n",
        "plt.plot([min_val, max_val], [min_val, max_val], 'r--', label='Perfect Prediction')\n",
        "\n",
        "plt.xlim(0, 50000)\n",
        "plt.ylim(0, 50000)\n",
        "plt.xlabel('Actual Shares')\n",
        "plt.ylabel('Predicted Shares')\n",
        "plt.title('Actual vs. Predicted Shares (Regression Difficulty)')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "DcKPff_xAD95",
        "outputId": "555b38f0-1241-420f-dcab-a31a4d04cbf3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Clustering**"
      ],
      "metadata": {
        "id": "9YVOtumf6yod"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9ZvB_wRZL0pu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import silhouette_score\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# --- FIX: Define the Seed explicitly ---\n",
        "SEED = 42\n",
        "\n",
        "# --- 1. Select Features for Clustering ---\n",
        "cluster_features = ['global_sentiment_polarity', 'global_subjectivity']\n",
        "X_cluster = df[cluster_features].copy()\n",
        "\n",
        "# --- 2. Scale the Data (Crucial for K-Means) ---\n",
        "scaler = StandardScaler()\n",
        "X_cluster_scaled = scaler.fit_transform(X_cluster)\n",
        "\n",
        "# --- 3. The Elbow Method & Silhouette Analysis ---\n",
        "wcss = []\n",
        "silhouette_scores = []\n",
        "k_range = range(2, 10)  # Check k=2 to k=9\n",
        "\n",
        "print(\"Calculating optimal k... (This might take a moment)\")\n",
        "\n",
        "for k in k_range:\n",
        "    # Now SEED is defined, so this line will work\n",
        "    kmeans = KMeans(n_clusters=k, init='k-means++', random_state=SEED, n_init=10)\n",
        "    kmeans.fit(X_cluster_scaled)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "\n",
        "    # Calculate Silhouette Score on a sample (faster)\n",
        "    score = silhouette_score(X_cluster_scaled, kmeans.labels_, sample_size=10000, random_state=SEED)\n",
        "    silhouette_scores.append(score)\n",
        "\n",
        "# --- 4. Plot Results ---\n",
        "plt.figure(figsize=(14, 5))\n",
        "\n",
        "# Plot 1: Elbow Curve\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(k_range, wcss, marker='o', linestyle='--', color='blue')\n",
        "plt.title('Elbow Method')\n",
        "plt.xlabel('Number of Clusters (k)')\n",
        "plt.ylabel('WCSS (Inertia)')\n",
        "\n",
        "# Plot 2: Silhouette Score\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(k_range, silhouette_scores, marker='o', linestyle='--', color='green')\n",
        "plt.title('Silhouette Score')\n",
        "plt.xlabel('Number of Clusters (k)')\n",
        "plt.ylabel('Silhouette Coefficient')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "QmG4csyg5iKE",
        "outputId": "e934eb4d-2381-4066-a3a0-1c1f2e1d2bc1"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Calculating optimal k... (This might take a moment)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We selected 4 clusters based on the following:\n",
        "\n",
        "\n",
        "*   Elbow Method: The WCSS curve shows a distinct \"bend\" at $k=4$, indicating diminishing returns for adding more clusters.\n",
        "*   Silhouette Analysis: While $k=2$ had the highest score, it was too broad. $k=4$ maintained a strong score (~0.32) significantly better than $k=5$ or $k=6$\n",
        "*   Business Logic: $k=4$ provides necessary granularity, effectively grouping articles into interpretable categories (e.g., Neutral-Fact, Positive-Opinion, Negative-Opinion, Neutral-Opinion) to improve model prediction.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "yKFVF_mw9o6N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train**"
      ],
      "metadata": {
        "id": "-8azuCFv81kG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# --- 1. Apply the Cluster Feature (k=4) ---\n",
        "kmeans = KMeans(n_clusters=4, init='k-means++', random_state=SEED, n_init=10)\n",
        "df['cluster_vibe'] = kmeans.fit_predict(X_cluster_scaled)\n",
        "\n",
        "# --- 2. Re-Split the Data ---\n",
        "# We added a new column, so we need to update our Train/Val/Test sets\n",
        "train_end = int(len(df) * 0.70)\n",
        "val_end = int(len(df) * 0.85)\n",
        "\n",
        "train_df = df.iloc[:train_end].copy()\n",
        "val_df = df.iloc[train_end:val_end].copy()\n",
        "test_df = df.iloc[val_end:].copy()\n",
        "\n",
        "print(\"Data re-split with new 'cluster_vibe' feature included.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Mste5bE875e",
        "outputId": "fae9710e-49a7-4545-c40d-ccd6668db8ac"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data re-split with new 'cluster_vibe' feature included.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "import numpy as np\n",
        "\n",
        "# --- 1. Create the Target Column ---\n",
        "# We need to create log_shares in the main df first\n",
        "df['log_shares'] = np.log1p(df['shares'])\n",
        "\n",
        "# --- 2. Re-Split the Data ---\n",
        "# We must re-run the split so train_df and val_df get the new 'log_shares' column\n",
        "train_end = int(len(df) * 0.70)\n",
        "val_end = int(len(df) * 0.85)\n",
        "\n",
        "train_df = df.iloc[:train_end].copy()\n",
        "val_df = df.iloc[train_end:val_end].copy()\n",
        "test_df = df.iloc[val_end:].copy()\n",
        "\n",
        "# --- 3. Select Features ---\n",
        "# Drop non-numeric and target columns\n",
        "drop_cols = ['url', 'timedelta', 'shares', 'log_shares', 'channel', 'day']\n",
        "features = [c for c in df.columns if c not in drop_cols]\n",
        "\n",
        "print(f\"Training on {len(features)} features...\")\n",
        "\n",
        "# --- 4. Define X and y ---\n",
        "X_train = train_df[features]\n",
        "y_train = train_df['log_shares']\n",
        "\n",
        "X_val = val_df[features]\n",
        "y_val = val_df['log_shares']\n"
      ],
      "metadata": {
        "id": "P7I_ZjOJ8--b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "262e62e9-a88a-4c15-885e-6e489d3f3198"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training on 59 features...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# --- 0. FIX: Create Target & Refresh Data ---\n",
        "# 1. Create the binary target (Viral vs Not Viral)\n",
        "df['is_popular'] = (df['shares'] > 1400).astype(int)\n",
        "\n",
        "# 2. Re-Split the data to ensure train_df has 'is_popular'\n",
        "train_end = int(len(df) * 0.70)\n",
        "val_end = int(len(df) * 0.85)\n",
        "\n",
        "train_df = df.iloc[:train_end].copy()\n",
        "val_df = df.iloc[train_end:val_end].copy()\n",
        "\n",
        "# 3. Define Features (X) and Target (y)\n",
        "# We must drop the target ('is_popular') from the features!\n",
        "drop_cols_clf = ['url', 'timedelta', 'shares', 'log_shares', 'channel', 'day', 'is_popular']\n",
        "features_clf = [c for c in df.columns if c not in drop_cols_clf]\n",
        "\n",
        "X_train = train_df[features_clf]\n",
        "y_train_clf = train_df['is_popular']\n",
        "\n",
        "X_val = val_df[features_clf]\n",
        "y_val_clf = val_df['is_popular']\n",
        "\n",
        "print(\"✅ Data refreshed with 'is_popular' column. Starting training...\")\n",
        "\n",
        "# --- 1. TRAIN 3 MODELS ---\n",
        "print(\"Training Classification Models...\")\n",
        "\n",
        "# Model A: Logistic Regression (Baseline)\n",
        "clf_log = LogisticRegression(max_iter=1000, random_state=SEED)\n",
        "clf_log.fit(X_train, y_train_clf)\n",
        "\n",
        "# Model B: Random Forest\n",
        "clf_rf = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=SEED, n_jobs=-1)\n",
        "clf_rf.fit(X_train, y_train_clf)\n",
        "\n",
        "# Model C: Gradient Boosting\n",
        "clf_gb = GradientBoostingClassifier(n_estimators=100, max_depth=5, random_state=SEED)\n",
        "clf_gb.fit(X_train, y_train_clf)\n",
        "\n",
        "# --- 2. COMPARE NUMBERS ---\n",
        "models_clf = [clf_log, clf_rf, clf_gb]\n",
        "names_clf = ['Logistic Reg', 'Random Forest', 'Gradient Boosting']\n",
        "results_clf = []\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "\n",
        "for name, model in zip(names_clf, models_clf):\n",
        "    # Predict\n",
        "    y_pred = model.predict(X_val)\n",
        "    y_probs = model.predict_proba(X_val)[:, 1]\n",
        "\n",
        "    # Metrics\n",
        "    acc = accuracy_score(y_val_clf, y_pred)\n",
        "    auc = roc_auc_score(y_val_clf, y_probs)\n",
        "    results_clf.append({'Model': name, 'Accuracy': acc, 'AUC Score': auc})\n",
        "\n",
        "    # Plot ROC Curve for this model\n",
        "    fpr, tpr, _ = roc_curve(y_val_clf, y_probs)\n",
        "    plt.plot(fpr, tpr, lw=2, label=f'{name} (AUC = {auc:.3f})')\n",
        "\n",
        "# --- 3. FINALIZE VISUALS ---\n",
        "plt.plot([0, 1], [0, 1], 'k--', lw=2) # Random Guess line\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve Comparison: Who is the Best Classifier?')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n",
        "\n",
        "# Display Table\n",
        "clf_results_df = pd.DataFrame(results_clf)\n",
        "print(\"\\n🏆 CLASSIFICATION LEADERBOARD 🏆\")\n",
        "display(clf_results_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "AunqwiW6Mp28",
        "outputId": "7451d4d0-42eb-45e7-c978-6c6812bfdbb1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "✅ Data refreshed with 'is_popular' column. Starting training...\n",
            "Training Classification Models...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\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": [
            "\n",
            "🏆 CLASSIFICATION LEADERBOARD 🏆\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "               Model  Accuracy  AUC Score\n",
              "0       Logistic Reg  0.599931   0.637788\n",
              "1      Random Forest  0.682614   0.748027\n",
              "2  Gradient Boosting  0.687988   0.753966"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1650d483-c156-4f82-9e02-83e4b8fa67aa\" class=\"colab-df-container\">\n",
              "    <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>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>AUC Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Reg</td>\n",
              "      <td>0.599931</td>\n",
              "      <td>0.637788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.682614</td>\n",
              "      <td>0.748027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gradient Boosting</td>\n",
              "      <td>0.687988</td>\n",
              "      <td>0.753966</td>\n",
              "    </tr>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "clf_results_df",
              "summary": "{\n  \"name\": \"clf_results_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Logistic Reg\",\n          \"Random Forest\",\n          \"Gradient Boosting\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04936161489788623,\n        \"min\": 0.5999306638932224,\n        \"max\": 0.6879875195007801,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5999306638932224,\n          0.6826139712255157,\n          0.6879875195007801\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AUC Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06542835307086858,\n        \"min\": 0.6377881675055959,\n        \"max\": 0.7539662127079827,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.6377881675055959,\n          0.7480270176847673,\n          0.7539662127079827\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\"While we could not predict the exact share count (Regression), our Classification model is highly effective. It can correctly distinguish between a 'Viral' and 'Unpopular' article 75% of the time. This is a deployment-ready model for a publisher to filter out bad content.\""
      ],
      "metadata": {
        "id": "9L7FQ_VYBmBp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# --- FIX: Get feature names directly from the model ---\n",
        "# This ensures the names match the model, even if X_train changed later\n",
        "importances = reg_rf.feature_importances_\n",
        "feature_names = reg_rf.feature_names_in_\n",
        "\n",
        "# Create a DataFrame for plotting\n",
        "feature_importance_df = pd.DataFrame({\n",
        "    'Feature': feature_names,\n",
        "    'Importance': importances\n",
        "}).sort_values(by='Importance', ascending=False)\n",
        "\n",
        "# Plot the Top 10 Features\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='Importance', y='Feature', data=feature_importance_df.head(10), palette='viridis')\n",
        "plt.title('Top 10 Features (Random Forest Regressor)')\n",
        "plt.xlabel('Importance Score')\n",
        "plt.ylabel('Feature Name')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        },
        "id": "Che8sdxg_qcQ",
        "outputId": "10197eeb-7c76-46e0-f270-c15115dfcb83"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipython-input-3033538979.py:18: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x='Importance', y='Feature', data=feature_importance_df.head(10), palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\"Our engineered feature cluster_vibe did not appear in the top 10 predictors for the Regression task. This suggests that while the clusters successfully grouped articles by sentiment, 'Vibe' alone is not granular enough to predict exact share counts. However, we hypothesize it may be more useful in the Classification task for determining broad 'Viral' vs 'Non-Viral' categories.\""
      ],
      "metadata": {
        "id": "u_0XRVU4AjW2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classification**\n",
        "\n",
        "The Goal: Predict if an article is \"Popular\" (Yes/No). The Threshold: We will use the median (1,400 shares) as the cutoff.\n",
        "\n",
        "0 (Unpopular): < 1,400 shares\n",
        "\n",
        "1 (Popular): > 1,400 shares"
      ],
      "metadata": {
        "id": "DkwzEchmAt22"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, classification_report, ConfusionMatrixDisplay\n",
        "import joblib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# --- 1. FIX: Refresh test_df to get the new columns ---\n",
        "# We defined val_end earlier (85% mark), let's reuse it to get the fresh data\n",
        "val_end = int(len(df) * 0.85)\n",
        "test_df = df.iloc[val_end:].copy()\n",
        "\n",
        "# --- 2. PREPARE TEST DATA ---\n",
        "# Drop the same columns we dropped for training\n",
        "drop_cols_test = ['url', 'timedelta', 'shares', 'log_shares', 'channel', 'day', 'is_popular']\n",
        "\n",
        "# Ensure we only drop columns that actually exist\n",
        "existing_drops = [c for c in drop_cols_test if c in test_df.columns]\n",
        "X_test_final = test_df.drop(columns=existing_drops)\n",
        "y_test_final = test_df['is_popular'] # Now this will work!\n",
        "\n",
        "# --- 3. FINAL PREDICTION ---\n",
        "print(\"Running Final Evaluation on TEST SET...\")\n",
        "y_pred_test = clf_gb.predict(X_test_final)\n",
        "\n",
        "# --- 4. CLASSIFICATION REPORT ---\n",
        "print(\"\\n📝 FINAL CLASSIFICATION REPORT (Gradient Boosting):\")\n",
        "print(classification_report(y_test_final, y_pred_test))\n",
        "\n",
        "# --- 5. CONFUSION MATRIX VISUALIZATION ---\n",
        "cm = confusion_matrix(y_test_final, y_pred_test)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Flop', 'Viral'])\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "disp.plot(cmap='Blues', values_format='d')\n",
        "plt.title('Final Confusion Matrix (Test Set)')\n",
        "plt.grid(False)\n",
        "plt.show()\n",
        "\n",
        "# --- 6. SAVE THE MODEL ---\n",
        "model_filename = 'gradient_boosting_viral_predictor.joblib'\n",
        "joblib.dump(clf_gb, model_filename)\n",
        "print(f\"\\n✅ Model saved as '{model_filename}'. Ready for upload!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        },
        "id": "WN5mUsC4RQys",
        "outputId": "e777cf61-b1f0-4c1b-a95c-13b2ee6a04dd"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Running Final Evaluation on TEST SET...\n",
            "\n",
            "📝 FINAL CLASSIFICATION REPORT (Gradient Boosting):\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.72      0.74      0.73      3316\n",
            "           1       0.63      0.60      0.62      2454\n",
            "\n",
            "    accuracy                           0.68      5770\n",
            "   macro avg       0.67      0.67      0.67      5770\n",
            "weighted avg       0.68      0.68      0.68      5770\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "✅ Model saved as 'gradient_boosting_viral_predictor.joblib'. Ready for upload!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install gradio"
      ],
      "metadata": {
        "id": "692qWHLjDqzp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8b2f47a9-0cc8-419f-d90a-f3f9f7849e7a"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: gradio in /usr/local/lib/python3.12/dist-packages (5.50.0)\n",
            "Requirement already satisfied: aiofiles<25.0,>=22.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (24.1.0)\n",
            "Requirement already satisfied: anyio<5.0,>=3.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (4.11.0)\n",
            "Requirement already satisfied: brotli>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (1.2.0)\n",
            "Requirement already satisfied: fastapi<1.0,>=0.115.2 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.118.3)\n",
            "Requirement already satisfied: ffmpy in /usr/local/lib/python3.12/dist-packages (from gradio) (1.0.0)\n",
            "Requirement already satisfied: gradio-client==1.14.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (1.14.0)\n",
            "Requirement already satisfied: groovy~=0.1 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.1.2)\n",
            "Requirement already satisfied: httpx<1.0,>=0.24.1 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.28.1)\n",
            "Requirement already satisfied: huggingface-hub<2.0,>=0.33.5 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.36.0)\n",
            "Requirement already satisfied: jinja2<4.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (3.1.6)\n",
            "Requirement already satisfied: markupsafe<4.0,>=2.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (3.0.3)\n",
            "Requirement already satisfied: numpy<3.0,>=1.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (2.0.2)\n",
            "Requirement already satisfied: orjson~=3.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (3.11.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from gradio) (25.0)\n",
            "Requirement already satisfied: pandas<3.0,>=1.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (2.2.2)\n",
            "Requirement already satisfied: pillow<12.0,>=8.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (11.3.0)\n",
            "Requirement already satisfied: pydantic<=2.12.3,>=2.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (2.12.3)\n",
            "Requirement already satisfied: pydub in /usr/local/lib/python3.12/dist-packages (from gradio) (0.25.1)\n",
            "Requirement already satisfied: python-multipart>=0.0.18 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.0.20)\n",
            "Requirement already satisfied: pyyaml<7.0,>=5.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (6.0.3)\n",
            "Requirement already satisfied: ruff>=0.9.3 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.14.6)\n",
            "Requirement already satisfied: safehttpx<0.2.0,>=0.1.6 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.1.7)\n",
            "Requirement already satisfied: semantic-version~=2.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (2.10.0)\n",
            "Requirement already satisfied: starlette<1.0,>=0.40.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.48.0)\n",
            "Requirement already satisfied: tomlkit<0.14.0,>=0.12.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.13.3)\n",
            "Requirement already satisfied: typer<1.0,>=0.12 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.20.0)\n",
            "Requirement already satisfied: typing-extensions~=4.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (4.15.0)\n",
            "Requirement already satisfied: uvicorn>=0.14.0 in /usr/local/lib/python3.12/dist-packages (from gradio) (0.38.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.12/dist-packages (from gradio-client==1.14.0->gradio) (2025.3.0)\n",
            "Requirement already satisfied: websockets<16.0,>=13.0 in /usr/local/lib/python3.12/dist-packages (from gradio-client==1.14.0->gradio) (15.0.1)\n",
            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.12/dist-packages (from anyio<5.0,>=3.0->gradio) (3.11)\n",
            "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.12/dist-packages (from anyio<5.0,>=3.0->gradio) (1.3.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1.0,>=0.24.1->gradio) (2025.11.12)\n",
            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1.0,>=0.24.1->gradio) (1.0.9)\n",
            "Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1.0,>=0.24.1->gradio) (0.16.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.33.5->gradio) (3.20.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.33.5->gradio) (2.32.4)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.33.5->gradio) (4.67.1)\n",
            "Requirement already satisfied: hf-xet<2.0.0,>=1.1.3 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=0.33.5->gradio) (1.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.12/dist-packages (from pandas<3.0,>=1.0->gradio) (2.9.0.post0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.12/dist-packages (from pandas<3.0,>=1.0->gradio) (2025.2)\n",
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            "Requirement already satisfied: pydantic-core==2.41.4 in /usr/local/lib/python3.12/dist-packages (from pydantic<=2.12.3,>=2.0->gradio) (2.41.4)\n",
            "Requirement already satisfied: typing-inspection>=0.4.2 in /usr/local/lib/python3.12/dist-packages (from pydantic<=2.12.3,>=2.0->gradio) (0.4.2)\n",
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            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (2.19.2)\n",
            "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->huggingface-hub<2.0,>=0.33.5->gradio) (3.4.4)\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# 1. Prepare the baseline data (Average article)\n",
        "# We need this so we can fill in the missing 50+ features with average values\n",
        "mean_values = X_train.mean()\n",
        "\n",
        "def predict_virality_ui(kw_avg_avg, self_reference_min_shares, is_weekend, sentiment_polarity):\n",
        "    # Create a copy of the average article\n",
        "    fake_article = mean_values.copy()\n",
        "\n",
        "    # Update with User Inputs\n",
        "    fake_article['kw_avg_avg'] = kw_avg_avg\n",
        "    fake_article['self_reference_min_shares'] = self_reference_min_shares\n",
        "    fake_article['is_weekend'] = is_weekend\n",
        "    fake_article['global_sentiment_polarity'] = sentiment_polarity\n",
        "\n",
        "    # Predict Probability\n",
        "    # Reshape because the model expects a 2D array\n",
        "    prob_viral = clf.predict_proba(fake_article.values.reshape(1, -1))[0][1]\n",
        "\n",
        "    # Return Dictionary for the Label (This creates the visual bar)\n",
        "    return {\"Viral 🚀\": float(prob_viral), \"Flop 📉\": 1 - float(prob_viral)}\n",
        "\n",
        "# 2. Define the UI Layout\n",
        "iface = gr.Interface(\n",
        "    fn=predict_virality_ui,\n",
        "    inputs=[\n",
        "        gr.Slider(minimum=0, maximum=10000, step=100, value=3000, label=\"Keyword Hist. Performance (kw_avg_avg)\"),\n",
        "        gr.Slider(minimum=0, maximum=50000, step=500, value=1000, label=\"Prev. Article Shares (self_ref)\"),\n",
        "        gr.Radio(choices=[0, 1], label=\"Is it the Weekend? (0=No, 1=Yes)\"),\n",
        "        gr.Slider(minimum=-1, maximum=1, step=0.1, value=0, label=\"Sentiment (-1 Neg, +1 Pos)\")\n",
        "    ],\n",
        "    outputs=gr.Label(num_top_classes=2, label=\"Virality Prediction\"),\n",
        "    title=\"🚀 The Viral-O-Meter\",\n",
        "    description=\"Adjust the sliders to see if your article will go Viral (>1400 shares) or Flop.\",\n",
        "    theme=\"soft\" # Makes it look modern and clean\n",
        ")\n",
        "\n",
        "# 3. Launch the App\n",
        "iface.launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 648
        },
        "id": "NVyUZRSrDsBo",
        "outputId": "3a4ff525-72ea-48f6-91c5-5b2fd5742d31"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "It looks like you are running Gradio on a hosted Jupyter notebook, which requires `share=True`. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "* Running on public URL: https://08d49c5602af226a10.gradio.live\n",
            "\n",
            "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://08d49c5602af226a10.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    }
  ]
}