{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZBiNdra-AOT2"
      },
      "source": [
        "# Import Library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "taECrFNE9yxz"
      },
      "outputs": [],
      "source": [
        "# Import necessary libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
        "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9MK_JVsjBjM-"
      },
      "source": [
        "# Import Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "XJVoE7SgBlDG",
        "outputId": "f60fab29-501d-4a88-b21b-a752d901790e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset shape: (500, 16)\n",
            "\n",
            "First 5 rows:\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>image_id</th>\n",
              "      <th>user_id</th>\n",
              "      <th>prompt</th>\n",
              "      <th>likes</th>\n",
              "      <th>shares</th>\n",
              "      <th>comments</th>\n",
              "      <th>platform</th>\n",
              "      <th>generation_time</th>\n",
              "      <th>gpu_usage</th>\n",
              "      <th>file_size_kb</th>\n",
              "      <th>resolution</th>\n",
              "      <th>style_accuracy_score</th>\n",
              "      <th>is_hand_edited</th>\n",
              "      <th>ethical_concerns_flag</th>\n",
              "      <th>creation_date</th>\n",
              "      <th>top_comment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>77ce5c72-eb45-4651-bcb1-c0677c0fceaf</td>\n",
              "      <td>6a7adf3d</td>\n",
              "      <td>Studio Ghibli-inspired ocean with giant fish</td>\n",
              "      <td>916</td>\n",
              "      <td>410</td>\n",
              "      <td>555</td>\n",
              "      <td>Reddit</td>\n",
              "      <td>4.80</td>\n",
              "      <td>49</td>\n",
              "      <td>1684</td>\n",
              "      <td>1024x1024</td>\n",
              "      <td>89</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2025-03-11</td>\n",
              "      <td>So nostalgic, feels like childhood memories. 🎥...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7d66c67f-0d11-4ef9-895c-d865ef11fe40</td>\n",
              "      <td>523b8706</td>\n",
              "      <td>Ghibli-style village at sunset</td>\n",
              "      <td>2965</td>\n",
              "      <td>1361</td>\n",
              "      <td>417</td>\n",
              "      <td>Reddit</td>\n",
              "      <td>11.11</td>\n",
              "      <td>81</td>\n",
              "      <td>2808</td>\n",
              "      <td>1024x1024</td>\n",
              "      <td>92</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>2025-03-11</td>\n",
              "      <td>Absolutely stunning! Love the details. 🎨 #5729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>d7978afd-3932-4cce-9a21-5f9bf2bc1f64</td>\n",
              "      <td>0e02592a</td>\n",
              "      <td>A lone traveler exploring an enchanted ruin</td>\n",
              "      <td>4727</td>\n",
              "      <td>655</td>\n",
              "      <td>785</td>\n",
              "      <td>Instagram</td>\n",
              "      <td>5.56</td>\n",
              "      <td>41</td>\n",
              "      <td>1800</td>\n",
              "      <td>2048x2048</td>\n",
              "      <td>61</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2025-03-06</td>\n",
              "      <td>Is this AI or hand-painted? Incredible! #8001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>cb34636a-a15c-4b15-999c-759dbb8896fe</td>\n",
              "      <td>9ed78a42</td>\n",
              "      <td>Spirited Away-style bustling market street</td>\n",
              "      <td>1629</td>\n",
              "      <td>1954</td>\n",
              "      <td>212</td>\n",
              "      <td>TikTok</td>\n",
              "      <td>12.45</td>\n",
              "      <td>88</td>\n",
              "      <td>479</td>\n",
              "      <td>2048x2048</td>\n",
              "      <td>76</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>2025-03-23</td>\n",
              "      <td>Is this AI or hand-painted? Incredible! #5620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7511fbb8-db05-4584-a3a4-e8bb525ed58b</td>\n",
              "      <td>69ec8f02</td>\n",
              "      <td>Magical Ghibli forest with floating lanterns</td>\n",
              "      <td>2573</td>\n",
              "      <td>1281</td>\n",
              "      <td>913</td>\n",
              "      <td>TikTok</td>\n",
              "      <td>4.80</td>\n",
              "      <td>64</td>\n",
              "      <td>1789</td>\n",
              "      <td>512x512</td>\n",
              "      <td>58</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2025-03-06</td>\n",
              "      <td>This looks straight out of a Ghibli movie! 🌟 #...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               image_id   user_id  \\\n",
              "0  77ce5c72-eb45-4651-bcb1-c0677c0fceaf  6a7adf3d   \n",
              "1  7d66c67f-0d11-4ef9-895c-d865ef11fe40  523b8706   \n",
              "2  d7978afd-3932-4cce-9a21-5f9bf2bc1f64  0e02592a   \n",
              "3  cb34636a-a15c-4b15-999c-759dbb8896fe  9ed78a42   \n",
              "4  7511fbb8-db05-4584-a3a4-e8bb525ed58b  69ec8f02   \n",
              "\n",
              "                                         prompt  likes  shares  comments  \\\n",
              "0  Studio Ghibli-inspired ocean with giant fish    916     410       555   \n",
              "1                Ghibli-style village at sunset   2965    1361       417   \n",
              "2   A lone traveler exploring an enchanted ruin   4727     655       785   \n",
              "3    Spirited Away-style bustling market street   1629    1954       212   \n",
              "4  Magical Ghibli forest with floating lanterns   2573    1281       913   \n",
              "\n",
              "    platform  generation_time  gpu_usage  file_size_kb resolution  \\\n",
              "0     Reddit             4.80         49          1684  1024x1024   \n",
              "1     Reddit            11.11         81          2808  1024x1024   \n",
              "2  Instagram             5.56         41          1800  2048x2048   \n",
              "3     TikTok            12.45         88           479  2048x2048   \n",
              "4     TikTok             4.80         64          1789    512x512   \n",
              "\n",
              "   style_accuracy_score is_hand_edited ethical_concerns_flag creation_date  \\\n",
              "0                    89            Yes                   Yes    2025-03-11   \n",
              "1                    92            Yes                    No    2025-03-11   \n",
              "2                    61             No                    No    2025-03-06   \n",
              "3                    76             No                    No    2025-03-23   \n",
              "4                    58             No                   Yes    2025-03-06   \n",
              "\n",
              "                                         top_comment  \n",
              "0  So nostalgic, feels like childhood memories. 🎥...  \n",
              "1     Absolutely stunning! Love the details. 🎨 #5729  \n",
              "2      Is this AI or hand-painted? Incredible! #8001  \n",
              "3      Is this AI or hand-painted? Incredible! #5620  \n",
              "4  This looks straight out of a Ghibli movie! 🌟 #...  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Load the dataset\n",
        "df = pd.read_csv('dataset/ai_ghibli_trend_dataset_v2.csv')\n",
        "print(f\"Dataset shape: {df.shape}\")\n",
        "print(\"\\nFirst 5 rows:\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qRbZogQMOBHN"
      },
      "source": [
        "# Data Preprocessing and Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fRPjDRY2OC_T",
        "outputId": "137bc025-8391-43be-80fe-5ba76ebf6437"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "============================================================\n",
            "FEATURE ENGINEERING\n",
            "============================================================\n",
            "Features created successfully!\n",
            "Total features: 25\n"
          ]
        }
      ],
      "source": [
        "# Feature Engineering\n",
        "print(\"=\"*60)\n",
        "print(\"FEATURE ENGINEERING\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "# Split resolution into width and height\n",
        "df[['width', 'height']] = df['resolution'].str.split('x', expand=True).astype(int)\n",
        "\n",
        "# Convert categorical binary features to numeric\n",
        "df['is_hand_edited'] = (df['is_hand_edited'] == 'Yes').astype(int)\n",
        "df['ethical_concerns_flag'] = (df['ethical_concerns_flag'] == 'Yes').astype(int)\n",
        "\n",
        "# Extract temporal features\n",
        "df['creation_date'] = pd.to_datetime(df['creation_date'])\n",
        "df['day_of_week'] = df['creation_date'].dt.dayofweek\n",
        "df['month'] = df['creation_date'].dt.month\n",
        "df['hour'] = df['creation_date'].dt.hour\n",
        "\n",
        "# Create derived features\n",
        "df['aspect_ratio'] = df['width'] / df['height']\n",
        "df['total_pixels'] = df['width'] * df['height']\n",
        "df['is_square'] = (df['width'] == df['height']).astype(int)\n",
        "df['is_weekend'] = (df['day_of_week'] >= 5).astype(int)\n",
        "\n",
        "print(f\"Features created successfully!\")\n",
        "print(f\"Total features: {df.shape[1]}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mWdj1slFZYSG"
      },
      "source": [
        "# Target Variable Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "B-k5NDAAhL1J",
        "outputId": "30045678-8915-41b6-85c8-0e40cf847f35"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "============================================================\n",
            "TARGET VARIABLE ANALYSIS\n",
            "============================================================\n",
            "\n",
            "Shares Statistics:\n",
            "Mean: 1040.18\n",
            "Median: 1092.00\n",
            "Std Dev: 562.67\n",
            "Min: 13\n",
            "Max: 1999\n",
            "Skewness: -0.14\n"
          ]
        },
        {
          "data": {
            "image/png": 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G+fn5SktLU1pamqRjx0RpaWnat2+fSktLdfXVV+unn37SW2+9pfLycmVmZiozM1MlJSWSjg1iPmfOHG3cuFG7d+/WW2+9pfvuu0833nijveB0ww03KCAgQGPHjtWWLVv03nvvae7cuQ5nQgEAADS0kxro3M/PT8OHD9fQoUP173//W5MnT9bf//53PfTQQ7rmmms0Y8YMtWjRol5tZ2ZmSvpr0M0KsbGx9nlVcccgnMcP4mcYhkwmk0ySTDKqjDfp2K8Grhj4z5nluXKZnuijJ9U0QKO7172zTiav+gxIWZfl+fj41Lrf1BbjqraOTTPc/nqsSx+dyevAgQP297XqpKeny1ZW7jXr/vi4hlr3f23fv5Zfl+U5s14tFouioqLqlFdV6rK+yktLtXfvXhmGY5xhGMrLy9OhQ4dq3dbOLtNbP6saeuBcb/zM+umnn9S/f3/744pC0ahRozRlyhQtWbJE0rEfCY+3atUq9evXT2azWe+++66mTJmi4uJiJSYm6r777nMoOIWHh+vLL79USkqKevTooaioKD322GMaN25cw3cQAADg/51UUeqnn37SggUL9O677yokJER///vfNXbsWP3xxx+aOnWqrrzySv3444+uytUp7hiE8/hB/PLy8nRmYoJiQqRg/+Iq40NDJL/EBOXl5Z30wHrOLM+Vy7TZbDp69KjOTExQtJv66Ek1DdDo7nXvrJPJqz4DUjq7PL9mZhV06qBWFl9FVBPnTIyr2jLJULhvqeJDJF83vh6d7aMz+01ubq6emfu88o9W344klZQUyxIWovAAm4L9i/+/FFe/vFy5HRvyvbBi+5oke3+dXZ6z6zU0yKz7771b4eHhTudVFWfXl9m3UNmhQZq/8A375VUVTCaTWsRGa9++P2QJC1FMYLksJ7kdvfWzqqEHznV2EE536tevX6VC5PFqmidJ3bt31/fff1/rcrp06aJvvvmmzvkBAAC4Sr2KUrNnz9bChQu1fft2XXrppXr99dd16aWX2g8WExMTtWjRIrVp06beiVUMspmVleVwtlVWVlalXwaP545BOI8fxK+wsFA796SrrINkCan6bjbWAmnvnnSFhYWd9MB6+fn5tS7Plcu02WzKzMzUzj3pKm3vnj56Uk0DNLp73TvrZPKqz4CUzi5v/+Fibdzyqyy9y1XSrOo4Z2Jc1ZZJhgxJ+wukPW58PTrbR2f2m/z8fKVt/U3R5w9XSGRslTGSdGD3Zm1a87o6DCpVib+5yqKUO9d9XfrorBPXfcX2zSn9q7/OLs+Z9VpwKEs7v/9Ivr6+tbblqn1if7ZVaVt+U49OyWreIsFhnklSeYiUcyRQP69epNCkYsVaTm47eutnVUMPnMuNUwAAADynXkWpF198UbfccotGjx5d7eV5MTExevXVV+udWGJiouLi4vTVV1/Zi1BWq1U//PCD7rzzzmqf565BOCsG8au47OCvC4MqM/TXpQwnm4Mzy/PEMl25PE+rboBGT6x7Z5xsXnUdkLIuy7PZbLXuN7XFuLYtk0deG872sba8KpYXHBmrsJiW1baVdzDTYZlVLdf9694d70smh/46uzxn1mtd23Llfh8UEV0pL5MMBfkXK8gS5dLt6K2fVQ05cG5j/8wCAABozOpVlNqxY0etMQEBARo1alSNMfn5+dq5c6f9ccVAnpGRkWrdurUmTJigJ598Um3btlViYqIeffRRxcfHa9iwYfVJGwAAAAAAAF6iXkWphQsXKjQ0VH/7298cpn/wwQcqLCystRhVoaaBPBctWqQHH3xQBQUFGjdunI4cOaILL7xQy5Yt41R7AAAAAACARq5e56xPnz69yjsQxcTE6KmnnnK6nYqBPE/8W7RokaRjp+tPmzZNmZmZKioq0ooVK3TWWWfVJ2UAAAAAAAB4kXoVpfbt26fExMRK0xMSErRv376TTgoAAAAAAABNW72KUjExMdq0aVOl6Rs3blTz5s1POikAAIDG5vfff9cff/xhf/zjjz9qwoQJevnllz2YFQAAgPeqV1Hq+uuv1z333KNVq1apvLxc5eXlWrlype69915dd911rs4RAADA691www1atWqVJCkzM1OXXHKJfvzxRz388MOaNm2ah7MDAADwPvUqSj3xxBPq1auXBgwYoKCgIAUFBWnQoEG6+OKL6zSmFAAAQFOxefNm9ezZU5L0/vvv6+yzz9batWv11ltv2cfLBAAAwF/qdfe9gIAAvffee3riiSe0ceNGBQUFqXPnzkpISHB1fgAAAI1CaWmpzGazJGnFihW64oorJEnt27dXRkaGJ1MDAADwSvUqSlU466yzuBseAACApE6dOmn+/PkaOnSoli9frieeeEKStH//fsbcBAAAqEK9ilLl5eVatGiRvvrqK2VnZ8tmsznMX7lypUuSAwAAaCxmzJihq666SrNmzdKoUaPUtWtXSdKSJUvsl/UBAADgL/UqSt17771atGiRhg4dqrPPPlsmk8nVeQEAADQq/fr104EDB2S1WtWsWTP79HHjxik4ONiDmQEAAHinehWl3n33Xb3//vu69NJLXZ0PAABAo2UYhtavX69du3bphhtuUFhYmAICAihKAQAAVKHeA52feeaZrs4FAACg0UpPT9fgwYO1b98+FRcX65JLLlFYWJhmzJih4uJizZ8/39MpAgAAeJV6FaXuv/9+zZ07Vy+88AKX7gEAAOjY8AbnnnuuNm7c6DCw+VVXXaXbbrvNg5kBaEriQk0KOvKbtN/H06k4Mgz5HToklWdIXvYdMejIb4oL9a6cABxTr6LUt99+q1WrVunzzz9Xp06d5O/v7zD/o48+cklyTUlpSYnS09NrjbNYLIqOjnZDRmgscnJyZLVaq52fnp6ustIyN2YE1J8z74UlJSUKCAioMcbZ/d6Z5XmiLfyltvVqGIbKy8sVExPjxqzq55tvvtHatWsr7b9t2rTRn3/+6aGsADQ1t/cIUIc1t0trPJ2JIx9JUZ5OohoddGy9AfA+9SpKRURE6KqrrnJ1Lk1WcX6u9u7ZrQkPTZHZbK4xNjIsWG8u/A+FKUg6VpC6ccytOpRXWG1M0dFC/fFnhlqXlroxM6DunHkvLC0p0Z/70tUyIVF+/tV/RDmz3zv73uvutvAXZ9aryWRSt45n6ckpj3l9Ycpms6m8vLzS9D/++ENhYWEeyAhAU/TS+hJd+9gidWjf3tOpOLAZhg4dOqTIyEj5eNmZUr9u26aXnrlBV3g6EQCV1KsotXDhQlfn0aSVFh+VzeSnqPOHq3l8QrVxBYeylJP6X1mtVopSkCRZrVYdyitUdNIIhUTGVhmTvWuz0n9foPIyvgTDuznzXpi9a7N2712gZj2vrPH90pn93tn3Xne3hb84s14LD2UpP2u9rFar1xelBg0apDlz5ujll1+WdKyglp+fr8cff5ybwwBwmcx8Q0cjzpLiu3k6FUc2m8p8s6WYGMnHuy4tPJppU2a+4ek0AFShXkUpSSorK9Pq1asd7i6zf/9+WSwWhYaGujLHJiO4WbQsMS1rjMlxUy5oXEIiY6vdd/IPZro5G+Dk1PReWLE/1/Z+WZf93lvbwl9qWq8mScpyazr19q9//UuDBw9Wx44dVVRUpBtuuEE7duxQVFSU3nnnHU+nBwAA4HXqVZTi7jIAAACOWrVqpY0bN+q9997Txo0blZ+fr7Fjx2rkyJEKCgrydHoAAABep15FKe4uAwAA8JfS0lK1b99eS5cu1ciRIzVy5EhPpwQAAOD16lWU4u4yAAAAf/H391dRUZGn0wAAAGhU6jUCHXeXAQAAcJSSkqIZM2aorKzM06kAAAA0CvUqSlXcXaYCd5cBAACnunXr1umjjz5S69atlZycrOHDhzv8OWvNmjW6/PLLFR8fL5PJpMWLFzvMNwxDjz32mFq0aKGgoCANHDhQO3bscIg5dOiQRo4cKYvFooiICI0dO1b5+fkOMZs2bdJFF12kwMBAtWrVSjNnzqx33wEAAOqjXkWpZ555Rt99953D3WUqLt2bMWOGq3MEAADwehERERoxYoSSk5MVHx+v8PBwhz9nFRQUqGvXrpo3b16V82fOnKnnnntO8+fP1w8//KCQkBAlJyc7XD44cuRIbdmyRcuXL9fSpUu1Zs0ajRs3zj7farVq0KBBSkhI0Pr16zVr1ixNmTJFL7/8cv1XAAAAQB3Va0ypli1bauPGjXr33Xe1adMm7i4DAABOeQsXLnRJO0OGDNGQIUOqnGcYhubMmaNHHnlEV155pSTp9ddfV2xsrBYvXqzrrrtOv/76q5YtW6Z169bp3HPPlSQ9//zzuvTSS/Wvf/1L8fHxeuutt1RSUqIFCxYoICBAnTp1UlpammbPnu1QvAIAAGhI9SpKSZKfn59uvPFGV+YCAACAGuzZs0eZmZkaOHCgfVp4eLh69eql1NRUXXfddUpNTVVERIS9ICVJAwcOlI+Pj3744QddddVVSk1NVZ8+fRxuWpOcnKwZM2bo8OHDatasWaVlFxcXq7i42P7YarVKOjbWqM1ma4juAjhBxWvNG193NptNhmF4XV6Sd683oKly9rVWr6LU66+/XuP8m2++uT7NAgAANGoffvih3n//fe3bt08lJSUO8zZs2HDS7WdmZkqSYmNjHabHxsba52VmZiomJsZhvp+fnyIjIx1iEhMTK7VRMa+qotT06dM1derUStNzcnK48yDgJocOHbL/m52d7eFsHNlsNuXm5sowDPn41GuUmAbjzesNaKry8vKciqtXUeree+91eFxaWqrCwkIFBAQoODiYohQAADjlPPfcc3r44Yc1evRoffLJJxozZox27dqldevWKSUlxdPpnbTJkydr4sSJ9sdWq1WtWrVSdHS0LBaLBzMDTh2RkZH2f08sPnuazWaTyWRSdHS01xWlvHm9AU1VYGCgU3H1KkodPny40rQdO3bozjvv1AMPPFCfJqtUXl6uKVOm6M0331RmZqbi4+M1evRoPfLIIzKZTC5bDgAAwMn697//rZdfflnXX3+9Fi1apAcffFCnn366HnvsMfuv9CcrLi5OkpSVlaUWLVrYp2dlZalbt272mBPPBCgrK9OhQ4fsz4+Li1NWVpZDTMXjipgTmc1mmc3mStN9fHy87gso0FRVvNa89XVnMpm8MjdvX29AU+Tsa81lr8i2bdvq6aefrnQW1cmYMWOGXnzxRb3wwgv69ddfNWPGDM2cOVPPP/+8y5YBAADgCvv27dMFF1wgSQoKCrKftn7TTTfpnXfecckyEhMTFRcXp6+++so+zWq16ocfflBSUpIkKSkpSUeOHNH69evtMStXrpTNZlOvXr3sMWvWrFFpaak9Zvny5WrXrl2Vl+4BAAA0BJeWif38/LR//36Xtbd27VpdeeWVGjp0qNq0aaOrr75agwYN0o8//uiyZQAAALhCXFyc/Yyo1q1b6/vvv5d0bHBywzCcbic/P19paWlKS0uzPz8tLU379u2TyWTShAkT9OSTT2rJkiX65ZdfdPPNNys+Pl7Dhg2TJHXo0EGDBw/Wbbfdph9//FHfffedxo8fr+uuu07x8fGSpBtuuEEBAQEaO3astmzZovfee09z5851uDwPAACgodXr8r0lS5Y4PDYMQxkZGXrhhRfUu3dvlyQmSRdccIFefvll/fbbbzrrrLO0ceNGffvtt5o9e7bLlgEAAOAKF198sZYsWaJzzjlHY8aM0X333acPP/xQP/30k4YPH+50Oz/99JP69+9vf1xRKBo1apT9ssCCggKNGzdOR44c0YUXXqhly5Y5jN3w1ltvafz48RowYIB8fHw0YsQIPffcc/b54eHh+vLLL5WSkqIePXooKipKjz32mMaNG+eCNQEAAOCcehWlKn6Jq1AxoN3FF1+sZ555xhV5SZL+8Y9/yGq1qn379vL19VV5ebn++c9/auTIkdU+xx23Kz7+dqeGYchkMskkyaSqfwU16dj1lDXFVMSZTKYab6XqzPIq2iovLdXevXtr/HXWYrEoKiqq2vl16aMzy3NmmXVx4MAB+zY+2eUdOHBAubm5ysvLU15eXqVxy9LT02UrK3fbupec29512b9OzMswjEr9LSkpcbhF+Inqsh5qy6suuZ9sW8emGU69zpzV0NvnRI113bujrb+2r1FtTGPvo+O8v/ZnV+blzteGJ/KqiivbfPnll+3tpaSkqHnz5lq7dq2uuOIK3X777U63069fvxo/P0wmk6ZNm6Zp06ZVGxMZGam33367xuV06dJF33zzjdN5AQAAuFq9ilINcVBYlffff19vvfWW3n77bXXq1ElpaWmaMGGC4uPjNWrUqCqf447bFR9/u9O8vDydmZigmBAp2L+4yni/ZmYVdOqgVhZfRVQTI0mhIZJfYoLy8vKqvVWpM8uTJLNvobJDgzR/4Rvy9/evfplBZt1/790KDw+vtq9Hjx7VmYkJiq5hmc4uz5llOis3N1fPzH1e+UerXw/OLq+irYKiErWIjVZGVk6lLwQlJcWyhIUoJrBcFjese8m57e3s/lVVXiaTyaG/ZWVlOpSTrebRsfL1862yHWfXgzN5OZu7K9oyyVC4b6niQyTfWl5nzmro7XOiuqz7wk7tFRXsozL/4v8vXdQvL2/bjtXFVWxfk2Tvrzfk1VBtVfS3dTOz8l2UlzOfQc5y9rPKmbzCQiTf2Gjl5+c3yG28nb1dsTNOHED3uuuu03XXXeey9gEAAJqaehWl3OWBBx7QP/7xD/sBXefOnZWenq7p06dXW5Ryx+2Kj7/daWFhoXbuSVdZB8kSUvmONJK0/3CxNm75VZbe5SppVnWMJFkLpL170hUWFlbtrUrz8/NrXZ4k7c+2Km3Lb+rRKVnNWyRUGVNwKEs7v/9Ivr6+1S7PZrMpMzNTO/ekq7R9DX10YnnOLtNZ+fn5Stv6m6LPH66QyNiTWl5FWzHnD1dMy1iVNVOl3+wP7N6stNWLFJpUrFhLw6/7irxctX9VlZdJUnmI7P3N3r1ZaSvXqMc1l1Sbu9PrwYm8nM7dBW2ZZMiQtL9A2lPL68xZDb19TlSXdb9pyzZ1SrapxGyusijlznXvjrYqtm9O6V/99Ya8Gqqtiv7uc2FeznwGOcvpzyon8sorkPyychQaGtogt/F29nbFzjpy5Ih+/PFHZWdnV/oR7+abb3bpsgAAABq7ehWl6jII5smM/1RYWFjpNoK+vr41nqnlrtsVV9zutOKSgr8upKjM0P9fBldDTEVcxSUP1eXqzPKOX2ZQRLTCYlrWe3nOLtOZ5dVlmc6oyCs4MtZlfQyOjFVQRJTCQip/kc87mOn0dvTEuq9PXiYZCvIvtve3oo815V7X9eDK3E++LVOD7IMNtX1OVN91X1Ws+9e9O9oyOfTXe/JqqLZMLs/Lna8NT+RVFVe2+emnn2rkyJHKz8+XxWJxuAzcZDJRlAIAADhBvYpSP//8s37++WeVlpaqXbt2kqTffvtNvr6+6t69uz3uxDF56uryyy/XP//5T7Vu3VqdOnXSzz//rNmzZ+uWW245qXYBAABc7f7779ctt9yip556SsHBwZ5OBwAAwOvVqyh1+eWXKywsTK+99pqaNWsmSTp8+LDGjBmjiy66SPfff79Lknv++ef16KOP6q677lJ2drbi4+N1++2367HHHnNJ+wAAAK7y559/6p577qEgBQAA4KR6FaWeeeYZffnll/aClCQ1a9ZMTz75pAYNGuSyolRYWJjmzJmjOXPmuKQ9AACAhpKcnKyffvpJp59+uqdTAQAAaBTqVZSyWq3KycmpND0nJ8eld7EBAADwZkuWLLH/f+jQoXrggQe0detWde7cudKdNK+44gp3pwcAAODV6lWUuuqqqzRmzBg988wz6tmzpyTphx9+0AMPPKDhw4e7NEEAAABvNWzYsErTpk2bVmmayWRSeXm5GzICAABoPOpVlJo/f77+/ve/64YbblBpaemxhvz8NHbsWM2aNculCQIAAHirmu4IDAAAgJrV6z7IwcHB+ve//62DBw/a78R36NAh/fvf/1ZISIircwQAAPBaqampWrp0qcO0119/XYmJiYqJidG4ceNUXFzsoewAAAC8V72KUhUyMjKUkZGhtm3bKiQkRIZhuCovAACARmHq1KnasmWL/fEvv/yisWPHauDAgfrHP/6hTz/9VNOnT/dghgAAAN6pXkWpgwcPasCAATrrrLN06aWXKiMjQ5I0duxYl915DwAAoDHYuHGjBgwYYH/87rvvqlevXnrllVc0ceJEPffcc3r//fc9mCEAAIB3qldR6r777pO/v7/27dun4OBg+/Rrr71Wy5Ytc1lyAAAA3u7w4cOKjY21P/766681ZMgQ++PzzjtPv//+uydSAwAA8Gr1Kkp9+eWXmjFjhlq2bOkwvW3btkpPT3dJYgAAAI1BbGys9uzZI0kqKSnRhg0bdP7559vn5+Xlyd/f31PpAQAAeK16FaUKCgoczpCqcOjQIZnN5pNOCgAAoLG49NJL9Y9//EPffPONJk+erODgYF100UX2+Zs2bdIZZ5zhwQwBAAC8U72KUhdddJFef/11+2OTySSbzaaZM2eqf//+LksOAADA2z3xxBPy8/NT37599corr+iVV15RQECAff6CBQs0aNAgD2YIAADgnfzq86SZM2dqwIAB+umnn1RSUqIHH3xQW7Zs0aFDh/Tdd9+5OkcAAACvFRUVpTVr1ig3N1ehoaHy9fV1mP/BBx8oNDTUQ9kBAAB4r3oVpc4++2z99ttveuGFFxQWFqb8/HwNHz5cKSkpatGihatzBAAAXiQnJ0dWq7Xa+enp6SorLXNjRt4hPDy8yumRkZFuzgQAAKBxqHNRqrS0VIMHD9b8+fP18MMPN0ROAADAS+Xk5OjGMbfqUF5htTFFRwv1x58Zal1a6sbMAAAA0NjUuSjl7++vTZs2NUQuAADAy1mtVh3KK1R00giFRMZWGZO9a7PSf1+g8jKKUgAAAKhevQY6v/HGG/Xqq6+6OhcAANBIhETGyhLTssq/4IgoT6cHAACARqBeY0qVlZVpwYIFWrFihXr06KGQkBCH+bNnz3ZJcgAAAAAAAGia6lSU2r17t9q0aaPNmzere/fukqTffvvNIcZkMrkuOwAAAAAAADRJdSpKtW3bVhkZGVq1apUk6dprr9Vzzz2n2Niqx5QAAAAAAAAAqlKnMaUMw3B4/Pnnn6ugoMClCQEAAKB6bdq0kclkqvSXkpIiSerXr1+leXfccYdDG/v27dPQoUMVHBysmJgYPfDAAyorK/NEdwAAwCmsXmNKVTixSAUAAICGtW7dOpWXl9sfb968WZdccon+9re/2afddtttmjZtmv1xcHCw/f/l5eUaOnSo4uLitHbtWmVkZOjmm2+Wv7+/nnrqKfd0AgAAQHUsSlX82nbiNAAAALhHdHS0w+Onn35aZ5xxhvr27WufFhwcrLi4uCqf/+WXX2rr1q1asWKFYmNj1a1bNz3xxBOaNGmSpkyZooCAgAbNHwAAoEKdilKGYWj06NEym82SpKKiIt1xxx2V7r730UcfuS5DAAAAVKmkpERvvvmmJk6c6PBD4VtvvaU333xTcXFxuvzyy/Xoo4/az5ZKTU1V586dHcYETU5O1p133qktW7bonHPOqXJZxcXFKi4utj+2Wq2SJJvNJpvN1hDdA3CCiteaN77ubDabDMPwurwk715vQFPl7GutTkWpUaNGOTy+8cYb6/J0AAAAuNDixYt15MgRjR492j7thhtuUEJCguLj47Vp0yZNmjRJ27dvt/9omJmZWekmNRWPMzMzq13W9OnTNXXq1ErTc3JyVFRU5ILeAKjNoUOH7P9mZ2d7OBtHNptNubm5MgxDPj51Grq4wXnzegOaqry8PKfi6lSUWrhwYb2SAQAAgOu9+uqrGjJkiOLj4+3Txo0bZ/9/586d1aJFCw0YMEC7du3SGWecUe9lTZ48WRMnTrQ/tlqtatWqlaKjo2WxWOrdLgDnRUZG2v+NiYnxcDaObDabTCa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            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Analyze the target variable\n",
        "print(\"=\"*60)\n",
        "print(\"TARGET VARIABLE ANALYSIS\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "# Define target and features\n",
        "y = df['shares']\n",
        "X = df.drop(columns=['image_id', 'user_id', 'prompt', 'shares', 'comments',\n",
        "                     'top_comment', 'resolution', 'creation_date'])\n",
        "\n",
        "# One-hot encode platform\n",
        "X = pd.get_dummies(X, columns=['platform'], prefix='platform')\n",
        "\n",
        "# Target statistics\n",
        "print(f\"\\nShares Statistics:\")\n",
        "print(f\"Mean: {y.mean():.2f}\")\n",
        "print(f\"Median: {y.median():.2f}\")\n",
        "print(f\"Std Dev: {y.std():.2f}\")\n",
        "print(f\"Min: {y.min()}\")\n",
        "print(f\"Max: {y.max()}\")\n",
        "print(f\"Skewness: {y.skew():.2f}\")\n",
        "\n",
        "# Visualize target distribution\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
        "\n",
        "ax1.hist(y, bins=50, edgecolor='black', alpha=0.7)\n",
        "ax1.set_xlabel('Shares')\n",
        "ax1.set_ylabel('Frequency')\n",
        "ax1.set_title('Distribution of Shares')\n",
        "ax1.grid(True, alpha=0.3)\n",
        "\n",
        "ax2.boxplot(y, vert=True)\n",
        "ax2.set_ylabel('Shares')\n",
        "ax2.set_title('Boxplot of Shares')\n",
        "ax2.grid(True, alpha=0.3)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-G8d_ZuXZedt"
      },
      "source": [
        "# Feature Analysis and Selection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TRbt0BQthNXO",
        "outputId": "1aabd86c-c700-4a5a-a798-130a73a009a6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "============================================================\n",
            "FEATURE CORRELATION ANALYSIS\n",
            "============================================================\n",
            "Top 15 Feature Correlations with Shares:\n",
            "                  feature  correlation\n",
            "19       platform_Twitter    -0.113105\n",
            "16     platform_Instagram     0.070970\n",
            "13           total_pixels     0.053401\n",
            "7                   width     0.050954\n",
            "8                  height     0.050954\n",
            "17        platform_Reddit     0.030825\n",
            "0                   likes    -0.029318\n",
            "5          is_hand_edited     0.028240\n",
            "9             day_of_week     0.024903\n",
            "3            file_size_kb    -0.020748\n",
            "6   ethical_concerns_flag     0.019647\n",
            "18        platform_TikTok     0.016843\n",
            "2               gpu_usage     0.015755\n",
            "15             is_weekend     0.015367\n",
            "4    style_accuracy_score     0.011279\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Feature correlation analysis\n",
        "print(\"=\"*60)\n",
        "print(\"FEATURE CORRELATION ANALYSIS\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "# Calculate correlations with target\n",
        "correlations = pd.DataFrame({\n",
        "    'feature': X.columns,\n",
        "    'correlation': [X[col].corr(y) for col in X.columns]\n",
        "}).sort_values('correlation', key=abs, ascending=False)\n",
        "\n",
        "print(\"Top 15 Feature Correlations with Shares:\")\n",
        "print(correlations.head(15))\n",
        "\n",
        "# Visualization of correlations\n",
        "plt.figure(figsize=(10, 8))\n",
        "top_features = correlations.head(15)\n",
        "colors = ['green' if x > 0 else 'red' for x in top_features['correlation']]\n",
        "plt.barh(range(len(top_features)), top_features['correlation'], color=colors, alpha=0.7)\n",
        "plt.yticks(range(len(top_features)), top_features['feature'])\n",
        "plt.xlabel('Correlation with Shares')\n",
        "plt.title('Top 15 Feature Correlations')\n",
        "plt.axvline(x=0, color='black', linestyle='-', linewidth=0.5)\n",
        "plt.grid(True, alpha=0.3)\n",
        "plt.gca().invert_yaxis()\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gt1ouI4BZkjE"
      },
      "source": [
        "# Advanced Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A1KULUuphPON",
        "outputId": "29ec939b-7ac0-40b9-d88a-f1933a37f153"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "============================================================\n",
            "CREATING ADVANCED FEATURES\n",
            "============================================================\n",
            "Total features after engineering: 31\n",
            "Features after removing multicollinearity: 29\n"
          ]
        }
      ],
      "source": [
        "# Create interaction and polynomial features\n",
        "print(\"=\"*60)\n",
        "print(\"CREATING ADVANCED FEATURES\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "# Log transform target (to handle skewness)\n",
        "y_log = np.log1p(y)  # log1p handles zeros safely\n",
        "\n",
        "# Create interaction features\n",
        "X['engagement_rate'] = X['likes'] / (X['total_pixels'] / 1000000 + 1)\n",
        "X['quality_engagement'] = X['style_accuracy_score'] * X['likes'] / 100\n",
        "X['file_density'] = X['file_size_kb'] / (X['total_pixels'] / 1000 + 1)\n",
        "X['gpu_efficiency'] = X['generation_time'] / (X['gpu_usage'] + 1)\n",
        "\n",
        "# Platform-specific features\n",
        "for platform in ['Twitter', 'TikTok', 'Reddit']:\n",
        "    if f'platform_{platform}' in X.columns:\n",
        "        X[f'{platform.lower()}_likes'] = X['likes'] * X[f'platform_{platform}']\n",
        "\n",
        "# Temporal cyclical features\n",
        "X['month_sin'] = np.sin(2 * np.pi * X['month'] / 12)\n",
        "X['month_cos'] = np.cos(2 * np.pi * X['month'] / 12)\n",
        "X['day_sin'] = np.sin(2 * np.pi * X['day_of_week'] / 7)\n",
        "X['day_cos'] = np.cos(2 * np.pi * X['day_of_week'] / 7)\n",
        "\n",
        "print(f\"Total features after engineering: {X.shape[1]}\")\n",
        "\n",
        "# Remove highly correlated features\n",
        "corr_matrix = X.corr().abs()\n",
        "upper_tri = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n",
        "to_drop = [column for column in upper_tri.columns if any(upper_tri[column] > 0.95)]\n",
        "X = X.drop(columns=to_drop)\n",
        "print(f\"Features after removing multicollinearity: {X.shape[1]}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x3AkuY7LBlTP"
      },
      "source": [
        "# Train-Test Split and Scaling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7o5a1IBIBoYr",
        "outputId": "5c5cbae8-6c2a-4b60-ca5a-b420b1a175c2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "============================================================\n",
            "DATA SPLITTING AND SCALING\n",
            "============================================================\n",
            "Training set: (400, 29)\n",
            "Test set: (100, 29)\n",
            "Data preprocessing completed!\n"
          ]
        }
      ],
      "source": [
        "# Split the data\n",
        "print(\"=\"*60)\n",
        "print(\"DATA SPLITTING AND SCALING\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "# Use both original and log-transformed targets\n",
        "X_train, X_test, y_train, y_test, y_log_train, y_log_test = train_test_split(\n",
        "    X, y, y_log, test_size=0.2, random_state=42\n",
        ")\n",
        "\n",
        "print(f\"Training set: {X_train.shape}\")\n",
        "print(f\"Test set: {X_test.shape}\")\n",
        "\n",
        "# Scale features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "print(\"Data preprocessing completed!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9p0hQpBBosr"
      },
      "source": [
        "# Training Multiple Regression Models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YdVJjloPhWrK",
        "outputId": "64a29ce2-52ea-4049-9966-c46555f76ae9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "============================================================\n",
            "TRAINING REGRESSION MODELS\n",
            "============================================================\n",
            "\n",
            "1. Linear Regression...\n",
            "2. Ridge Regression...\n",
            "3. Lasso Regression...\n",
            "4. Random Forest Regressor...\n",
            "5. Gradient Boosting Regressor...\n",
            "\n",
            "All models trained successfully!\n"
          ]
        }
      ],
      "source": [
        "# Train multiple regression models\n",
        "print(\"=\"*60)\n",
        "print(\"TRAINING REGRESSION MODELS\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "# Dictionary to store results\n",
        "results = {}\n",
        "\n",
        "# Model 1: Linear Regression\n",
        "print(\"\\n1. Linear Regression...\")\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train_scaled, y_train)\n",
        "y_pred_lr = lr.predict(X_test_scaled)\n",
        "results['Linear Regression'] = {\n",
        "    'predictions': y_pred_lr,\n",
        "    'r2': r2_score(y_test, y_pred_lr),\n",
        "    'mae': mean_absolute_error(y_test, y_pred_lr),\n",
        "    'rmse': np.sqrt(mean_squared_error(y_test, y_pred_lr))\n",
        "}\n",
        "\n",
        "# Model 2: Ridge Regression\n",
        "print(\"2. Ridge Regression...\")\n",
        "ridge = Ridge(alpha=10.0)\n",
        "ridge.fit(X_train_scaled, y_train)\n",
        "y_pred_ridge = ridge.predict(X_test_scaled)\n",
        "results['Ridge Regression'] = {\n",
        "    'predictions': y_pred_ridge,\n",
        "    'r2': r2_score(y_test, y_pred_ridge),\n",
        "    'mae': mean_absolute_error(y_test, y_pred_ridge),\n",
        "    'rmse': np.sqrt(mean_squared_error(y_test, y_pred_ridge))\n",
        "}\n",
        "\n",
        "# Model 3: Lasso Regression\n",
        "print(\"3. Lasso Regression...\")\n",
        "lasso = Lasso(alpha=1.0)\n",
        "lasso.fit(X_train_scaled, y_train)\n",
        "y_pred_lasso = lasso.predict(X_test_scaled)\n",
        "results['Lasso Regression'] = {\n",
        "    'predictions': y_pred_lasso,\n",
        "    'r2': r2_score(y_test, y_pred_lasso),\n",
        "    'mae': mean_absolute_error(y_test, y_pred_lasso),\n",
        "    'rmse': np.sqrt(mean_squared_error(y_test, y_pred_lasso))\n",
        "}\n",
        "\n",
        "# Model 4: Random Forest\n",
        "print(\"4. Random Forest Regressor...\")\n",
        "rf = RandomForestRegressor(n_estimators=100, max_depth=10, random_state=42)\n",
        "rf.fit(X_train_scaled, y_train)\n",
        "y_pred_rf = rf.predict(X_test_scaled)\n",
        "results['Random Forest'] = {\n",
        "    'predictions': y_pred_rf,\n",
        "    'r2': r2_score(y_test, y_pred_rf),\n",
        "    'mae': mean_absolute_error(y_test, y_pred_rf),\n",
        "    'rmse': np.sqrt(mean_squared_error(y_test, y_pred_rf))\n",
        "}\n",
        "\n",
        "# Model 5: Gradient Boosting\n",
        "print(\"5. Gradient Boosting Regressor...\")\n",
        "gb = GradientBoostingRegressor(n_estimators=100, max_depth=5, random_state=42)\n",
        "gb.fit(X_train_scaled, y_train)\n",
        "y_pred_gb = gb.predict(X_test_scaled)\n",
        "results['Gradient Boosting'] = {\n",
        "    'predictions': y_pred_gb,\n",
        "    'r2': r2_score(y_test, y_pred_gb),\n",
        "    'mae': mean_absolute_error(y_test, y_pred_gb),\n",
        "    'rmse': np.sqrt(mean_squared_error(y_test, y_pred_gb))\n",
        "}\n",
        "\n",
        "print(\"\\nAll models trained successfully!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1GgeDPqbZyH0"
      },
      "source": [
        "# Model Comparison Table"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yEScyc2GiX86",
        "outputId": "777ce395-1d4b-4304-bb25-b01ebc6a90c5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "============================================================\n",
            "MODEL PERFORMANCE COMPARISON\n",
            "============================================================\n",
            "\n",
            "Regression Models Performance:\n",
            "            Model  R² Score        MAE       RMSE\n",
            "    Random Forest -0.085036 518.768500 593.750611\n",
            " Ridge Regression -0.086767 528.589291 594.223994\n",
            " Lasso Regression -0.087646 528.573249 594.464161\n",
            "Linear Regression -0.099142 531.413278 597.597560\n",
            "Gradient Boosting -0.235788 537.630957 633.656677\n",
            "\n",
            "Best performing model: Random Forest\n"
          ]
        }
      ],
      "source": [
        "# Create comparison table\n",
        "print(\"=\"*60)\n",
        "print(\"MODEL PERFORMANCE COMPARISON\")\n",
        "print(\"=\"*60)\n",
        "\n",
        "comparison_df = pd.DataFrame({\n",
        "    'Model': results.keys(),\n",
        "    'R² Score': [results[m]['r2'] for m in results],\n",
        "    'MAE': [results[m]['mae'] for m in results],\n",
        "    'RMSE': [results[m]['rmse'] for m in results]\n",
        "})\n",
        "\n",
        "comparison_df = comparison_df.sort_values('R² Score', ascending=False)\n",
        "print(\"\\nRegression Models Performance:\")\n",
        "print(comparison_df.to_string(index=False))\n",
        "\n",
        "# Find best model\n",
        "best_model_name = comparison_df.iloc[0]['Model']\n",
        "print(f\"\\nBest performing model: {best_model_name}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DWJr9Ff1Z2Uc"
      },
      "source": [
        "# Visualization of Results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "fd_dYczSiwO3",
        "outputId": "cbe5047f-3d69-474b-c991-5b77ed6a3a26"
      },
      "outputs": [
        {
          "data": {
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n1bp1ax06dEjDhw9XZGSkvvjiCy1fvjxXmcuXL1eHDh1Uv359jR49WhaLxXay9r///a8aNmxYoLYAAPKW1/mSgh7HCnKeoHnz5ho+fLjee+89Pf/887rpppskyfbvpTRv3lxly5bVrFmzbGOUr7/+Wv7+/nmeY7Jarbr77rv122+/aeDAgbrpppu0detWTZo0Sf/8849++OEHW96PPvpINWvW1N133y13d3f9/PPPeuyxx2S1WjVkyBC7cvfs2aP77rtP/fv3V58+ffTZZ5+pb9++ql+/vmrWrJlv/aOjoyVln9944YUXcp2juNDixYvVtWtX1ahRQ+PHj9epU6fUr1+/a3r0iiPOmbz66qt68cUX1a1bNz3yyCM6ceKE3n//fTVv3lxbtmxRUFDQZeuRmJiYa7xlGIZKlixpl5bXmOpSdXvkkUc0Y8YM3XfffXryySe1fv16jR8/Xjt37sx1jm/Xrl3q0aOHBg0apAEDBlz2IqgzZ86oY8eO6tatm3r06KE5c+Zo8ODB8vT01MMPPyx/f3/de++9+vrrr/X222/Lzc3Ntu5XX30l0zT14IMPXnbbSNkB7scff1zJycny9/dXZmam5s6dq5EjR+Y5zWfO3yG33nqrxo8fr7i4OL377rtavXq1XZ9cyT5V0L9vgDyZAJzCtGnTTEnmxo0bzQ8++MAsUaKEmZKSYpqmad5///3m7bffbpqmaUZHR5udOnWyrffDDz+YksxXXnnFrrz77rvPNAzD3LNnj2mapvnHH3+YkszHHnvMLl/Pnj1NSebo0aNtaf379zdLly5tnjx50i5v9+7dzcDAQFu99u/fb0oyp02bdsm2rVixwpRkfvbZZ+aJEyfMY8eOmQsXLjQrV65sGoZhbtiwwZY3KyvL7NWrl9m6dWvz7NmzBdhypvnSSy+Zkkw/Pz+zQ4cO5quvvmpu2rQpV77PPvvMlGS+/fbbuZZZrVbTNAu+PU3TNCWZFovF3L59u13egm6/vIwYMcKUZG7ZsuWy7TZN0+zcubPp6elp7t2715Z27Ngxs0SJEmbz5s1taTn7V7t27WxtNU3TjImJMQ3DMB999FFbWmZmplm2bFmzRYsWtrScvvbx8TGPHDliS1+/fr0pyRwxYoQtLa/2ffXVV6Yk89dff7WljR492pRk9ujRI1f+nGU5Jk2aZEoyT5w4ke+2eOedd0xJ5pdffmlLS09PN2NiYkx/f38zKSnJri0lS5Y0T58+bcv7448/mpLMn3/+Od/PAAAUrqsdD/33v/81JZkzZ860K2/hwoW50vM6Tg0aNMj09fU1U1NTbWktWrQwJZmff/65LS0tLc2MiIgwu3btWqD23HnnnWbTpk1t76dOnWq6u7ub8fHxdvn69OljSjKHDRtmS7NarWanTp1MT09P2/Hv8ccfNwMCAszMzMx8P/OJJ54wJZn//e9/bWlnz541K1SoYJYvX97MysoyTTPvcVyLFi3sjv8X1i86Otr2/sSJE7nGjzlat25t1q5d225bWq1Ws0mTJmaVKlXyrXcOSfm+vvrqK7u6SjKnTJlit35OuwICAnJt57p165phYWHmqVOnbGl//vmnabFYzN69e9vSLjVGyUvOflu/fn0zPT3dlj5hwgRTkvnjjz+appk9zi1btqz5wAMP2K3/9ttvm4ZhmPv27bvk5/Tp08f08/MzTTN7bNq6dWtbuREREebYsWNt7Z84caJtvYLuEzljqTlz5tjynTt3zqxcubIpyVyxYoVpmtn9WaVKlVzjypSUFLNChQpm27Ztc22b/fv3X3Y7AkBxlPM7uXTpUvPEiRPm4cOHzW+++cYMDQ01vby8zMOHD9vyFvQ4VtDzBHPnzrX7fb+cnOPjiRMnzKeeesqsXLmybdmtt95q9uvXzzTN7GP5kCFDbMu++OIL02Kx2B2HTNM0p0yZYkoyV69ebUvLa5zWrl07s2LFinZp0dHRuc4xxMfHm15eXuaTTz55yXakpKSY1apVMyWZ0dHRZt++fc1PP/3UjIuLy5W3bt26ZunSpc2EhARb2uLFi23r5sg573XxtsxrvHWt50wOHDhgurm5ma+++qpd+tatW013d/dc6RfL2efyenl5eeWqe15jqvzqlnPu8ZFHHrFLf+qpp0xJ5vLly21pOX24cOHCS9Y3R87Y76233rKlpaWl2b4XOWOwRYsWmZLMBQsW2K1fp06dPMe5F8vZf0+fPm16enqaX3zxhWmapvnLL7+YhmGYBw4csPsumGb2uaewsDCzVq1a5vnz521lzZs3z5RkvvTSS7a0gu5TV/L3TX5jeBRvTPUJOKGcqaDmzZuns2fPat68eflOlzB//ny5ublp+PDhdulPPvmkTNPUggULbPkk5cp38d1npmnq22+/1V133SXTNHXy5Enbq127dkpMTNTmzZuvql0PP/ywQkNDFRkZqfbt2ysxMVFffPGFbr31VlueTz/9VF988YVSUlJ05513qmXLlpe962/s2LGaNWuW6tWrp0WLFuk///mP6tevr1tuucVuyotvv/1WpUqV0rBhw3KVkXNFVUG3Z44WLVqoRo0atvfXuv2SkpIkSSVKlLhkm6XsOwMWL16szp07q2LFirb00qVLq2fPnvrtt99s5eXo37+/3dVjjRo1kmma6t+/vy3Nzc1NDRo00L59+3J9ZufOne2ew9iwYUM1atTItn9JspuPPeeK/caNG0tSnm1/9NFHL9vWnCujfvzxR1mt1jzzzJ8/XxEREerRo4ctzcPDQ8OHD1dycrJWrVpll/+BBx6wu3oyZyqVvNoNALj+rmQ8NHfuXAUGBqpt27Z2x9769evL399fK1assOW98Dh19uxZnTx5UrfddptSUlLspqySJH9/f7tnzXl6eqphw4YFOlacOnVKixYtsjsude3aVYZhaM6cOXmuM3ToUNv/c2YOSE9P19KlSyVlHw/PnTtnNwXqxebPn6+GDRvaTQXl7++vgQMH6sCBA7YpLwvD6dOntXz5cnXr1s22bU+ePKlTp06pXbt22r17d57THV3snnvu0ZIlS3K9br/9drt8Xl5e6tevX55ldO3a1TYlqSQdP35cf/zxh/r27Wt3pXqdOnXUtm1bu7FMjoKMUS40cOBAuyuvBw8eLHd3d1vZFotFDz74oH766SedPXvWlm/mzJlq0qSJKlSoUODP6tmzp1auXGm7WzA2NvaSfy8UZJ+YP3++SpcubfdMbl9fXw0cONCuvD/++MM2reipU6ds/Xzu3Dm1bt1av/76a77jNQBA3tq0aaPQ0FBFRUXpvvvuk5+fn3766SfbXUAFPY5dzXmCq9GzZ0/t2bNHGzdutP17qXHaTTfdpOrVq9uN01q1aiVJ+Y7Tcu5Ia9Gihfbt26fExES7cmvUqGH3aJjQ0FBVq1btsuM0Hx8frV+/Xk8//bSk7Du1+vfvr9KlS2vYsGG2KSlztnmfPn0UGBhoW79t27Z254Gu1LWeM/nuu+9ktVrVrVs3u+0ZERGhKlWq2G3PS5k8eXKusdbF57yk3GOqS9UtZz8cOXKkXfqTTz4pSbmmGa9QoYLatWtXoPpKkru7uwYNGmR77+npqUGDBik+Pl6bNm2SlP1dioyM1MyZM235tm3bpr/++uuKniEdHBys9u3b66uvvpKUPbtCkyZNbHeMXuj3339XfHy8HnvsMXl7e9vSO3XqpOrVq9vafSX71JX8fQPkhak+AScUGhqqNm3aaNasWUpJSVFWVpbdH+AXOnjwoCIjI3MFinKmZzh48KDtX4vFokqVKtnlu/g2+hMnTighIUFTp07V1KlT8/zM+Pj4q2rXSy+9pNtuu802hefs2bNlsdhffzBgwAANGDDgisvu0aOHevTooaSkJK1fv17Tp0/XrFmzdNddd2nbtm3y9vbW3r17Va1aNbm75//TV9DtmePiEzTXuv1y5pK/8GRQfk6cOKGUlJQ8p0K46aabZLVadfjwYbspLsqVK2eXL2egERUVlSs9rznDq1SpkiutatWqdicwT58+rbFjx2r27Nm52nrxQF3KvQ3z8sADD+j//u//9Mgjj+i5555T69at1aVLF9133322fejgwYOqUqVKrn0qv767eFvkBAGZKx0AnMOVjId2796txMTEXM/2zXHh8Wj79u164YUXtHz58lwnvi4+TpUtWzbXdEvBwcH666+/Llv/r7/+WhkZGapXr5727NljS2/UqJFmzpyZa7oqi8Vid4JOyj7GSv+b7uuxxx7TnDlz1KFDB5UpU0Z33HGHunXrpvbt29vWOXjwYK6pviX742F+zyW5Vnv27JFpmnrxxRf14osv5pknPj7e7iKivJQtW1Zt2rS57OeVKVPGburPC108vsgZB+Q3blq0aJHOnTsnPz+/fMu4nIvHSf7+/ipdurSt/ySpd+/eeuONN/T999+rd+/e2rVrlzZt2mT3fJqC6Nixo0qUKKGvv/5af/zxh2699VZVrlzZ7rNyFHSfOHjwoCpXrpxrn794m+3evVtS9jML85OYmJhrOn8AQP4mT56sqlWrKjExUZ999pl+/fVXeXl52ZYX9Dh29uzZKz5PcDXq1aun6tWra9asWQoKClJERIQtkHex3bt3a+fOnfkGjy4cp61evVqjR4/W2rVrlZKSYpcvMTHRLlhy8d/0UvY4rSB/0wcGBmrChAmaMGGCDh48qGXLlunNN9/UBx98oMDAQL3yyiu2bZ7XeZBq1apd9UXx13rOZPfu3TJNM896SSrw9I8NGzZUgwYNLpvvUuOhvMZbFotFlStXtkuPiIhQUFDQZc+pXU5kZKTdWE2yHy83btzYdqHVRx99pJSUFPn6+mrmzJny9va2PdeyoHr27KlevXrp0KFD+uGHH/J91velvp/Vq1fXb7/9ZpevIPvUlfx9A+SFwB/gpHr27KkBAwYoNjZWHTp0KND83I6Qc3XuQw89lO8f8znPcLlStWvXtp3E6dy5s1JSUjRgwAA1a9YsV+DpagUEBKht27Zq27atPDw8NGPGDK1fv14tWrRwSPkXu/BKLenat1/16tUlSVu3blXdunUdU8kLXDi/+eXSzQI8yyYv3bp105o1a/T000+rbt268vf3l9VqVfv27fO8+vvibZgXHx8f/frrr1qxYoV++eUXLVy4UF9//bVatWqlxYsX59uuS8lvnattNwDA8Qo6HrJarQoLC7O7svdCOSeaEhIS1KJFCwUEBGjcuHGqVKmSvL29tXnzZj377LO5jlPXcqzIqUvTpk3zXL5v375cgb7LCQsL0x9//KFFixZpwYIFWrBggaZNm6bevXtrxowZV1RWXgzDyLNtWVlZBVo/Z/s99dRT+V69ffGJoGtxqTFEQcYX11L+1apRo4bq16+vL7/8Ur1799aXX34pT09PdevW7YrK8fLyUpcuXTRjxgzt27dPY8aMcXhd85PTzxMnTsx3vOrv73/d6gMAruDCIEznzp3VrFkz9ezZU7t27XLa39SePXvqo48+UokSJfTAAw/kugg3h9VqVe3atfX222/nuTznfNDevXvVunVrVa9eXW+//baioqLk6emp+fPna9KkSQ4dp10oOjpaDz/8sO69915VrFhRM2fO1CuvvHJFZeT3nMC8xlDXes7EarXKMAwtWLAgz23g6P3lasZbl3puYkHLvha9e/fWxIkT9cMPP6hHjx6aNWuW7rzzTrvAcUHcfffd8vLyUp8+fZSWlnbF47VrUdC/b4D8EPgDnNS9996rQYMGad26dfr666/zzRcdHa2lS5fq7Nmzdnep5UxVlXMLenR0tKxWq+2utxy7du2yKy80NFQlSpRQVlZWga60vhavv/66vv/+e7366qtXfJVzQTRo0EAzZszQ8ePHJUmVKlXS+vXrlZGRke8VUAXdnvm51u3XoUMHubm56csvv1SvXr0u+1m+vr65+jCnvhaLxWEB1Rw5V3hf6J9//rE9RPzMmTNatmyZxo4dq5deeumS610pi8Wi1q1bq3Xr1nr77bf12muv6T//+Y9WrFihNm3aKDo6Wn/99ZesVqvdHxwF7TsAgPMp6HioUqVKWrp0qZo2bXrJEwgrV67UqVOn9N1336l58+a29P379zu03vv379eaNWs0dOjQXBcfWa1W9erVS7NmzdILL7xgl75v3z7bVctS9jFWku04K2VPaXTXXXfprrvuktVq1WOPPaaPP/5YL774oipXrqzo6Oh8xwbSpY+HwcHBeU6PdfHV2fmdzMkJZHp4eBT6OPJK5bQ7v21TqlSpXFeQX6ndu3fbTUeanJys48ePq2PHjnb5evfurZEjR+r48eOaNWuWOnXqdFV3x/Xs2VOfffaZLBaLunfvnm++gu4T0dHR2rZtm0zTtOvji9fNmUEkICDA6foZAFyBm5ubxo8fr9tvv10ffPCBnnvuuQIfx7y9vQt8nqCgwZn89OzZUy+99JKOHz+uL774It98lSpV0p9//qnWrVtf8jN//vlnpaWl6aeffrK7m+96TWkYHBysSpUqadu2bZL+d3zM63zGxds35ziekJBgl37xGMoR50wqVaok0zRVoUIFu3GjM8g597h7927bzAKSFBcXp4SEhGs+L3Ps2LFcMzTkNV6uVauW6tWrp5kzZ6ps2bI6dOiQ3n///Sv+PB8fH3Xu3FlffvmlOnTooFKlSuWZ78Lv58V3vu7atcturCUVbJ8q6N83QH54xh/gpPz9/fXRRx9pzJgxuuuuu/LN17FjR2VlZemDDz6wS580aZIMw1CHDh0kyfbve++9Z5fvnXfesXvv5uamrl276ttvv7UNdi504sSJq2lOnipVqqSuXbtq+vTpio2NvaoyUlJStHbt2jyX5cxNnhPo7Nq1q06ePJlrW0n/uyKsoNszP9e6/aKiojRgwAAtXrw4z0GJ1WrVW2+9pSNHjsjNzU133HGHfvzxR7tpneLi4jRr1iw1a9bMNnWoo/zwww92z+bZsGGD1q9fb9suOVebXXyF3cX72ZU6ffp0rrScK8xz5t/v2LGjYmNj7U4MZ2Zm6v3335e/v3+h3fUJACg8BR0PdevWTVlZWXr55ZdzLcvMzLSdhMnrOJWenq4PP/zQofXOuTL3mWee0X333Wf36tatm1q0aJHn1bsXjj9M09QHH3wgDw8PtW7dWlL2cwMvZLFYbDMJXHg83LBhg9346Ny5c5o6darKly9/yWfSVKpUSX///bfdeOXPP//U6tWr7fL5+vpKyn1yKywsTC1bttTHH39su/DqQo4cR16p0qVLq27dupoxY4Zdvbdt26bFixfnCs5djalTpyojI8P2/qOPPlJmZmau8WOPHj1kGIYef/xx7du374qeN3Oh22+/XS+//LI++OADRURE5JuvoPtEx44ddezYMX3zzTe2fCkpKbmmr69fv74qVaqkN998U8nJybk+ryj7GQBcRcuWLdWwYUO98847Sk1NLfBx7ErOE+QETy4+nhdUpUqV9M4772j8+PFq2LBhvvm6deumo0eP6pNPPsm17Pz58zp37pyt7pL9OC0xMVHTpk27qvrl588//9TJkydzpR88eFA7duywnUO6cJtfOAXnkiVLcj0zOTo6Wm5ubvr111/t0i8eYzrinEmXLl3k5uamsWPH5irHNM1c48XrKWc/vLg9OXd7durU6ZrKz8zM1Mcff2x7n56ero8//lihoaGqX7++Xd5evXpp8eLFeuedd1SyZMnLns/Lz1NPPaXRo0fnO429lH3jQVhYmKZMmWIbk0vZ5yV37txpa/eV7FMF/fsGyA93/AFO7FLPzchx11136fbbb9d//vMfHThwQDfffLMWL16sH3/8UU888YTtity6deuqR48e+vDDD5WYmKgmTZpo2bJlds+cyfH6669rxYoVatSokQYMGKAaNWro9OnT2rx5s5YuXZpnEOZqPf3005ozZ47eeecdvf7661e8fkpKipo0aaLGjRurffv2ioqKUkJCgn744Qf997//VefOnVWvXj1J2VdXf/755xo5cqQ2bNig2267TefOndPSpUv12GOP6Z577inw9ryUa91+b731lvbu3avhw4fru+++05133qng4GAdOnRIc+fO1d9//227qvuVV17RkiVL1KxZMz322GNyd3fXxx9/rLS0tHznHr8WlStXVrNmzTR48GClpaXZBlDPPPOMpOwrv5s3b64JEyYoIyNDZcqU0eLFi6/5Topx48bp119/VadOnRQdHa34+Hh9+OGHKlu2rJo1ayZJGjhwoD7++GP17dtXmzZtUvny5fXNN99o9erVeuedd3I9txEAcGMoyHioRYsWGjRokMaPH68//vhDd9xxhzw8PLR7927NnTtX7777ru677z41adJEwcHB6tOnj4YPHy7DMPTFF184fJrnmTNnqm7duvneeX/33Xdr2LBh2rx5s2655RZJkre3txYuXKg+ffqoUaNGWrBggX755Rc9//zztql8HnnkEZ0+fVqtWrVS2bJldfDgQb3//vuqW7eu7arq5557Tl999ZU6dOig4cOHKyQkRDNmzND+/fv17bff5jsNlyQ9/PDDevvtt9WuXTv1799f8fHxmjJlimrWrGn3PEQfHx/VqFFDX3/9tapWraqQkBDVqlVLtWrV0uTJk9WsWTPVrl1bAwYMUMWKFRUXF6e1a9fqyJEj+vPPPy+7/f755x99+eWXudLDw8PVtm3by66fn4kTJ6pDhw6KiYlR//79df78eb3//vsKDAx0yFSZ6enpat26tbp166Zdu3bpww8/VLNmzXT33Xfb5QsNDVX79u01d+5cBQUFXfVJMIvFYnfXaH4Kuk8MGDBAH3zwgXr37q1NmzapdOnS+uKLL2yB3gs/9//+7//UoUMH1axZU/369VOZMmV09OhRrVixQgEBAfr555+vqk0AgP95+umndf/992v69Ol69NFHC3wcK+h5grp168rNzU1vvPGGEhMT5eXlpVatWuX7TLG8PP7445fN06tXL82ZM0ePPvqoVqxYoaZNmyorK0t///235syZo0WLFqlBgwa64447bDMbDBo0SMnJyfrkk08UFhaW5wVFV2vJkiUaPXq07r77bjVu3Fj+/v7at2+fPvvsM6Wlpdlty/Hjx6tTp05q1qyZHn74YZ0+fVrvv/++atasaXfxS2BgoO6//369//77MgxDlSpV0rx583I9h80R50wqVaqkV155RaNGjdKBAwfUuXNnlShRQvv379f333+vgQMH6qmnnrpsOQsWLLDd/X+hJk2aXPF09Dluvvlm9enTR1OnTrVNsb9hwwbNmDFDnTt3tpsZ4WpERkbqjTfe0IEDB1S1alXbs46nTp2aa2avnj176plnntH333+vwYMHF/jZh3m16eabb75kHg8PD73xxhvq16+fWrRooR49eiguLk7vvvuuypcvrxEjRtjyFnSfKujfN0C+TABOYdq0aaYkc+PGjZfMFx0dbXbq1Mku7ezZs+aIESPMyMhI08PDw6xSpYo5ceJE02q12uU7f/68OXz4cLNkyZKmn5+fedddd5mHDx82JZmjR4+2yxsXF2cOGTLEjIqKMj08PMyIiAizdevW5tSpU2159u/fb0oyp02bdsk6r1ixwpRkzp07N8/lLVu2NAMCAsyEhIRLlpOXjIwM85NPPjE7d+5sRkdHm15eXqavr69Zr149c+LEiWZaWppd/pSUFPM///mPWaFCBVu77rvvPnPv3r22PAXdnpLMIUOG5Fmvgmy/S8nMzDT/7//+z7ztttvMwMBA08PDw4yOjjb79etnbtmyxS7v5s2bzXbt2pn+/v6mr6+vefvtt5tr1qyxy5Pf/jV69GhTknnixAm79D59+ph+fn629zl9PXHiRPOtt94yo6KiTC8vL/O2224z//zzT7t1jxw5Yt57771mUFCQGRgYaN5///3msWPHcu1n+X32hctyLFu2zLznnnvMyMhI09PT04yMjDR79Ohh/vPPP3brxcXFmf369TNLlSplenp6mrVr1861f17Ylovl9V0AAFw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",
            "text/plain": [
              "<Figure size 1800x800 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Comprehensive visualization\n",
        "fig, axes = plt.subplots(1, 3, figsize=(18, 8))\n",
        "\n",
        "# 1. Model Performance Comparison\n",
        "ax = axes[0]\n",
        "x_pos = np.arange(len(comparison_df))\n",
        "colors = plt.cm.viridis(np.linspace(0, 1, len(comparison_df)))\n",
        "bars = ax.bar(x_pos, comparison_df['R² Score'], color=colors, alpha=0.8)\n",
        "ax.set_xlabel('Models')\n",
        "ax.set_ylabel('R² Score')\n",
        "ax.set_title('Model R² Score Comparison')\n",
        "ax.set_xticks(x_pos)\n",
        "ax.set_xticklabels(comparison_df['Model'], rotation=45, ha='right')\n",
        "ax.grid(True, alpha=0.3)\n",
        "ax.axhline(y=0, color='red', linestyle='--', alpha=0.5)\n",
        "\n",
        "# Add value labels\n",
        "for bar, value in zip(bars, comparison_df['R² Score']):\n",
        "    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01,\n",
        "            f'{value:.3f}', ha='center', va='bottom')\n",
        "\n",
        "# 2. MAE Comparison\n",
        "ax = axes[1]\n",
        "bars = ax.bar(x_pos, comparison_df['MAE'], color=colors, alpha=0.8)\n",
        "ax.set_xlabel('Models')\n",
        "ax.set_ylabel('MAE')\n",
        "ax.set_title('Mean Absolute Error by Model')\n",
        "ax.set_xticks(x_pos)\n",
        "ax.set_xticklabels(comparison_df['Model'], rotation=45, ha='right')\n",
        "ax.grid(True, alpha=0.3)\n",
        "\n",
        "# 3. RMSE Comparison (continued)\n",
        "ax = axes[2]\n",
        "bars = ax.bar(x_pos, comparison_df['RMSE'], color=colors, alpha=0.8)\n",
        "ax.set_xlabel('Models')\n",
        "ax.set_ylabel('RMSE')\n",
        "ax.set_title('Root Mean Square Error by Model')\n",
        "ax.set_xticks(x_pos)\n",
        "ax.set_xticklabels(comparison_df['Model'], rotation=45, ha='right')\n",
        "ax.grid(True, alpha=0.3)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QNCcnPHs5WX6"
      },
      "source": [
        "# Best Model: Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "FU9AabI1vUJF",
        "outputId": "93da3ca3-03cf-40aa-9f47-d4997f15ae8d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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lSxetXbtWS5cu1R133OF+cAEAAACeQkNDPVrsnOr1lyRNnDhRU6dO1eTJk7V48WIVFBToyy+/POF+Bw4cqE8++USvvPKKfv/9d7399tsKDw9XUlKS/v3vf0uSNmzYoNzcXPfDmxMmTNAHH3ygt956S7/99ptGjhypW265RT/99JMk18MD1157rfr06aPVq1frtttu0z//+c8TxtG8eXO1a9dOkydP9pg/ZcoUdezYUU2aNDnja+jybNmyRb169dJ1112ntWvX6tNPP9XixYs1fPhwSVJmZqZGjBihxx9/XBs2bNCcOXPUuXPn094fAABAIHv//fcVFxenZcuW6Z577tHdd9+tG264QR07dtTKlSvVo0cPDRgwQPv37/d43+jRozVx4kQtX75c8fHx6tOnj3tooxUrVujGG29Uv3799J///Efjx4/XI488ckzjqxdeeEHnn3++Vq1apUceeUTLli2TJP3www/Kzc3VjBkzJFX8vu2J7rn+97//VefOnWWz2fTjjz9qxYoVuvXWW93DVU2bNk2PPvqonnrqKf3+++96+umn9cgjj+j999/3epkDqCYMAKjhOnbsaLz00kuGYRjGoUOHjLi4OGPBggXu5enp6Ub//v2P+/6UlBRj0qRJHvPGjRtnnH/++R7zJk2aZKSkpBx3O6WlpUZERITxzTffuOdJMr788svjvueLL74woqOjjZCQEKNjx47G2LFjjTVr1nisI8l4+OGH3dN2u92QZHz33XfH3e6wYcOM6667zj09aNAgIyEhwSguLnbP+/DDD43GjRsbTqfTPa+4uNgIDQ01vv/+eyM/P9+QZCxcuPC4+wEAAAhUgwYNMq666irDMAzD6XQa8+bNM2w2m/HAAw+4l5/q9ZdhGEa9evWM5557zr380KFDxtlnn+3el2EYRpcuXYx7773XMAzD2LBhgyHJmDdvXrlxLliwwJBk7N692z3v4MGDRlhYmLFkyRKPdYcOHWrcdNNNhmEYxtixY42mTZt6LB8zZswx2zraW2+9ZYSHhxtFRUWGYRhGYWGhERYWZrz77rvlrn+ya+jy4l+1apUhydi6das77jvuuMNju4sWLTKCgoKMAwcOGP/+97+NyMhIo7Cw8LhxAwAAoHxHXvd26dLFuOiii9zLSkpKjFq1ahkDBgxwz8vNzTUkGUuXLjUMo+x6bvr06e518vPzjdDQUOPTTz81DMMwbr75ZqN79+4e+x09erTH9WhKSopx9dVXe6yzdetWQ5KxatWqEx7D8a45T3TPdezYsUb9+vUNh8NR7jbPOecc4+OPP/aY98QTTxjp6eknjAVAzUVLeQA12oYNG7Rs2TLddNNNkiSLxaK+ffvqvffec6+zevVqXXrppV7f9/bt23X77berYcOGioqKUmRkpOx2u3Jyciq8jeuuu05///23vv76a/Xq1UsLFy5U69atj3kKtEWLFu7XtWrVUmRkpHbs2OGe9/rrr6tNmzaKj49XeHi43nnnnWPiaN68uaxWq3t6zZo12rx5syIiItyt9mNiYnTw4EFt2bJFMTExGjx4sHr27Kk+ffro5ZdfPqWeBAAAAGq6WbNmKTw8XCEhIbrsssvUt29fjR8/3r38VK+/9u7dq9zcXLVv3979HovForZt2x43htWrV8tsNqtLly4Vjnvz5s3av3+/unfv7o4jPDxcH3zwgbs7zt9//90jDklKT08/6bZvuukmlZaWuntt+vTTTxUUFKS+fftK8s419NHWrFmjqVOnehxLz5495XQ6tXXrVnXv3l0pKSlKS0vTgAEDNG3atGNabgEAAKBijrxPaTabFRsbq+bNm7vnJSQkSJLHvUvJ81oyJiZGjRs31u+//y7Jde154YUXeqx/4YUXatOmTSotLXXPO9F18ZEqes15onuuq1evVqdOnRQcHHzM9vft26ctW7Zo6NChHtegTz75pEf39gACi8XXAQBAZXrvvfdUUlKixMRE9zzDMGSz2fTaa68pKipKoaGhp7zdoKCgY8buPNyd0mGDBg1Sfn6+Xn75ZaWkpMhmsyk9Pd2jy9KKCAkJUffu3dW9e3c98sgjuu222zRu3Dj3eJ+Sjrn4M5lMcjqdkqTp06frgQce0MSJE5Wenq6IiAg9//zz+vXXXz3eU6tWLY9pu92uNm3aaNq0acfEFB8fL8nV1eiIESM0Z84cffrpp3r44Yc1b948dejQ4ZSOEQD+v737jan5/eM4/uorNS3JWKYmTP6c5KTTDWND2pmZO6iGrSVOyNJqJsONipjCsOX/sITJn7Ek82fLipUi1mTknJGMydIOdmw2jd+N1vk58uek39lv6/t8bN051/W5+lx153Wu9+f6XADQF82aNUsHDx6Uj4+PgoOD5e3t+hX8b/JXT/1N1nU4HJI6j3kKCQlxafP19f2r++gSEBCghIQEFRUVyWKxqKioSAsXLpS/v7+knmfof/7p3GvwfTb/MZc7HA6lpqYqIyOj2/WhoaHy8fHRgwcPVFlZqRs3bignJ0ebNm3SvXv3FBgY2Kv5AgAA/Nv8bJ3y+8+6jr7sWrv8X/oxX/+Ku5nzd2uuv8vZXXn6yJEj3R5k7devn1v3CKDvoSgPoM/q6OjQiRMntGvXLs2ePdulbf78+SopKdGqVatkNBpVUVGhZcuW/XQcHx8flycupc5F0dbWVn379s0ZJBsaGlz6VFdX68CBA5o7d66kznM337171+t5hYeHq7S01O3+1dXVmjZtmtLS0pyfufNEpslk0tmzZxUUFKSAgIBf9ouKilJUVJQ2btyoqVOn6vTp0xTlAQAA1LkoGBYW5nZ/d/LX8OHDVVdX5zzzvKOjQ/fv35fJZPpp/0mTJunr16+qqqqS2Wzu1t61U//7vBseHi5fX1+9fPnylzvsDQaDysrKXD6rra398yQlpaSkKCYmRuXl5aqpqdHOnTudbT3N0F0PK7x580aDBw+W1D2Xm0wmPX78+Lf/C29vb5nNZpnNZuXm5iowMFA3b95UXFycW3MCAABA79TW1io0NFSSZLfbZbVaZTAYJHVmz+rqapf+1dXVGjdu3G+L3D/Lul3X9nbd1mg0qri4WF++fOlWvB82bJiCg4P1/PlzJSYm9mhcAH0Xr68H0GeVl5fLbrcrJSVFERERLj/x8fHOV9jn5uaqpKREubm5evLkiRobG7V9+3bnOKNGjdKtW7f0+vVrZziLiYlRW1ubduzYoWfPnmn//v26evWqy+8fO3asTp48qSdPnqiurk6JiYk92qnU3t6u2NhYnTp1Sg8fPlRzc7POnz+vHTt2aN68eW6PM3bsWNXX1+v69euyWq3Kzs7WvXv3/nhdYmKihg4dqnnz5un27dtqbm5WZWWlMjIy9OrVKzU3N2vjxo26c+eOWlpadOPGDdlsNmdYBgAAQM/8KX9JUmZmpgoKClRaWqqmpialpaXp/fv3vxxz1KhRSk5OlsViUWlpqXPMrtfHjxw5Ul5eXiovL1dbW5scDocGDhyorKwsrVmzRsXFxXr27JkePHigvXv3qri4WJK0atUq2Ww2rVu3Tk+fPtXp06e7HbH0KzNmzFBYWJiWLFmiCRMmaNq0ac62nmbosLAwjRgxQps2bZLNZtOVK1e0a9culz7r169XTU2N0tPT1dDQIJvNpkuXLik9PV1S5/eGwsJCNTQ0qKWlRSdOnNDXr181fvx4t+YDAACA3svLy1NFRYUePXqkpUuXaujQoZo/f74kae3ataqoqNCWLVtktVpVXFysffv2KSsr67djBgUFacCAAbp27Zrevn2rDx8+SOr9uq0kpaen6+PHj1q8eLHq6+tls9l08uRJPX36VJK0efNm5efnq7CwUFarVY2NjSoqKtLu3bt7/scB0CdQlAfQZx07dkxms1mDBg3q1hYfH6/6+no9fPhQMTExOn/+vMrKyjR58mTFxsbq7t27zr55eXl68eKFxowZ49yJYzAYdODAAe3fv1+RkZG6e/dutxB47Ngx2e12mUwmJSUlKSMjQ0FBQW7fv7+/v6ZMmaI9e/ZoxowZioiIUHZ2tlasWKF9+/a5PU5qaqri4uK0aNEiTZkyRe3t7S675n/Fz89Pt27dUmhoqOLi4mQwGJSSkqLPnz8rICBAfn5+ampqUnx8vMaNG6eVK1dq9erVSk1NdfveAAAA8F9/yl9S54JkUlKSkpOTnUcTLViw4LfjHjx4UAkJCUpLS9OECRO0YsUKffr0SZIUEhKizZs3a8OGDRo2bJizUL1lyxZlZ2crPz9fBoNBc+bM0ZUrVzR69GhJna99v3DhgkpLSxUZGalDhw5p27Ztbs3Ty8tLFotFdrtdFovFpa2nGbp///4qKSlRU1OTjEajtm/frq1bt7r0MRqNqqqqktVq1fTp0xUVFaWcnBznEVeBgYG6ePGiYmNjZTAYdOjQIZWUlGjixIluzQcAAAC9V1BQoMzMTEVHR6u1tVWXL1927nQ3mUw6d+6czpw5o4iICOXk5CgvL8/leM+f8fb2VmFhoQ4fPqzg4GDnRqferttK0pAhQ3Tz5k05HA7NnDlT0dHROnLkiHPX/PLly3X06FEVFRVp0qRJmjlzpo4fP+7M0wD+fby+/XgoMgAAAAAAAAAAAOBhlZWVmjVrlux2uwIDA//ftwMAHsNOeQAAAAAAAAAAAAAAPISiPAAAAAAAAAAAAAAAHsLr6wEAAAAAAAAAAAAA8BB2ygMAAAAAAAAAAAAA4CEU5QEAAAAAAAAAAAAA8BCK8gAAAAAAAAAAAAAAeAhFeQAAAAAAAAAAAAAAPISiPAAAAAAAAAAAAAAAHkJRHgAAAAAAAAAAAAAAD6EoDwAAAAAAAAAAAACAh1CUBwAAAAAAAAAAAADAQ/4DrVMAPz2y7wAAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 2500x800 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Comprehensive visualization\n",
        "fig, axes = plt.subplots(1, 3, figsize=(25, 8))\n",
        "\n",
        "# 4. Best Model: Actual vs Predicted\n",
        "ax = axes[0]\n",
        "best_predictions = results[best_model_name]['predictions']\n",
        "ax.scatter(y_test, best_predictions, alpha=0.5, s=30)\n",
        "ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)\n",
        "ax.set_xlabel('Actual Shares')\n",
        "ax.set_ylabel('Predicted Shares')\n",
        "ax.set_title(f'{best_model_name}: Actual vs Predicted')\n",
        "ax.grid(True, alpha=0.3)\n",
        "\n",
        "# 5. Residual Plot for Best Model\n",
        "ax = axes[1]\n",
        "residuals = y_test - best_predictions\n",
        "ax.scatter(best_predictions, residuals, alpha=0.5, s=30)\n",
        "ax.axhline(y=0, color='red', linestyle='--')\n",
        "ax.set_xlabel('Predicted Values')\n",
        "ax.set_ylabel('Residuals')\n",
        "ax.set_title(f'{best_model_name}: Residual Plot')\n",
        "ax.grid(True, alpha=0.3)\n",
        "\n",
        "# 6. Feature Importance (for tree-based models)\n",
        "ax = axes[2]\n",
        "if best_model_name in ['Random Forest', 'Gradient Boosting']:\n",
        "    model = rf if best_model_name == 'Random Forest' else gb\n",
        "    feature_importance = pd.DataFrame({\n",
        "        'feature': X.columns,\n",
        "        'importance': model.feature_importances_\n",
        "    }).sort_values('importance', ascending=False).head(10)\n",
        "\n",
        "    y_pos = np.arange(len(feature_importance))\n",
        "    ax.barh(y_pos, feature_importance['importance'], alpha=0.8)\n",
        "    ax.set_yticks(y_pos)\n",
        "    ax.set_yticklabels(feature_importance['feature'])\n",
        "    ax.set_xlabel('Importance')\n",
        "    ax.set_title('Top 10 Feature Importances')\n",
        "    ax.invert_yaxis()\n",
        "else:\n",
        "    # For linear models, show coefficients\n",
        "    model = lr if best_model_name == 'Linear Regression' else (ridge if best_model_name == 'Ridge Regression' else lasso)\n",
        "    coef_df = pd.DataFrame({\n",
        "        'feature': X.columns,\n",
        "        'coefficient': model.coef_\n",
        "    }).sort_values('coefficient', key=abs, ascending=False).head(10)\n",
        "\n",
        "    y_pos = np.arange(len(coef_df))\n",
        "    colors_coef = ['green' if x > 0 else 'red' for x in coef_df['coefficient']]\n",
        "    ax.barh(y_pos, coef_df['coefficient'], color=colors_coef, alpha=0.7)\n",
        "    ax.set_yticks(y_pos)\n",
        "    ax.set_yticklabels(coef_df['feature'])\n",
        "    ax.set_xlabel('Coefficient Value')\n",
        "    ax.set_title('Top 10 Coefficients by Magnitude')\n",
        "    ax.axvline(x=0, color='black', linestyle='-', linewidth=0.5)\n",
        "    ax.invert_yaxis()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1TEJk9craU8n"
      },
      "source": [
        "# Final Model Insights"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dX0RUq7KagJw"
      },
      "source": [
        "POSSIBLE REASONS FOR MODEL PERFORMANCE:\n",
        "1. Low feature correlations suggest missing important predictors\n",
        "2. The relationship between features and shares may be highly non-linear\n",
        "3. External factors not captured in the dataset may drive virality\n",
        "4. Possible data quality issues or synthetic data patterns\n",
        "\n",
        "RECOMMENDATIONS FOR IMPROVEMENT:\n",
        "1. Collect additional features (user follower count, posting time, hashtags)\n",
        "2. Try more advanced models (XGBoost, Neural Networks)\n",
        "3. Feature engineering focusing on user engagement patterns\n",
        "4. Consider time-series aspects of virality\n",
        "5. Investigate outliers and data quality issues"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lf-wrybgaish"
      },
      "source": [
        "# Save Models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ax8GI5zaalkF",
        "outputId": "7ee95963-c3ae-4149-acae-471962674149"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "All models and scaler have been saved successfully!\n"
          ]
        }
      ],
      "source": [
        "import joblib\n",
        "\n",
        "# Save all models and the scaler\n",
        "models_to_save = {\n",
        "    'linear_regression': lr,\n",
        "    'ridge_regression': ridge,\n",
        "    'lasso_regression': lasso,\n",
        "    'random_forest': rf,\n",
        "    'gradient_boosting': gb\n",
        "}\n",
        "\n",
        "# Save each model\n",
        "for model_name, model in models_to_save.items():\n",
        "    joblib.dump(model, f'models/{model_name}.joblib')\n",
        "\n",
        "# Save the scaler\n",
        "joblib.dump(scaler, 'models/scaler.joblib')\n",
        "\n",
        "print(\"All models and scaler have been saved successfully!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IxqiL3sz28by"
      },
      "source": [
        "# Prediction Function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TIy5twgB48tl",
        "outputId": "3b6794da-579b-4f2a-ddfe-591b2c4be9d3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loaded: Linear Regression\n",
            "Loaded: Ridge Regression\n",
            "Loaded: Lasso Regression\n",
            "Loaded: Random Forest\n",
            "Loaded: Gradient Boosting\n",
            "Loaded: scaler.joblib\n",
            "\n",
            "✅ All models and scaler loaded successfully!\n",
            "Model expects 29 features.\n"
          ]
        }
      ],
      "source": [
        "# Dictionary to hold the loaded model objects\n",
        "all_models = {}\n",
        "model_names = [\n",
        "    'Linear Regression', 'Ridge Regression', 'Lasso Regression',\n",
        "    'Random Forest', 'Gradient Boosting'\n",
        "]\n",
        "\n",
        "try:\n",
        "    # Load all the regression models\n",
        "    for name in model_names:\n",
        "        filename = f\"models/{name.lower().replace(' ', '_')}.joblib\"\n",
        "        all_models[name] = joblib.load(filename)\n",
        "        print(f\"Loaded: {name}\")\n",
        "\n",
        "    # Load the scaler ONCE, after the loop\n",
        "    scaler = joblib.load('models/scaler.joblib')\n",
        "    print(\"Loaded: scaler.joblib\")\n",
        "\n",
        "    models_loaded = True\n",
        "    print(\"\\n✅ All models and scaler loaded successfully!\")\n",
        "\n",
        "    # Get the feature names the model was trained on from the scaler\n",
        "    expected_columns = scaler.feature_names_in_\n",
        "    print(f\"Model expects {len(expected_columns)} features.\")\n",
        "\n",
        "except FileNotFoundError as e:\n",
        "    print(f\"\\n❌ ERROR: Could not find a model file: {e}\")\n",
        "    print(\"Please make sure all '.joblib' files are in the 'models/' directory.\")\n",
        "    models_loaded = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "jHDEssBjarnA"
      },
      "outputs": [],
      "source": [
        "def predict_shares_all_models(likes, generation_time, gpu_usage, file_size_kb,\n",
        "                              width, height, style_accuracy_score,\n",
        "                              is_hand_edited, ethical_concerns_flag,\n",
        "                              day_of_week, month, hour, platform):\n",
        "\n",
        "    # --- 1. Create a dictionary with the input data ---\n",
        "    sample_data = {\n",
        "        'likes': likes,\n",
        "        'style_accuracy_score': style_accuracy_score,\n",
        "        'generation_time': generation_time,\n",
        "        'gpu_usage': gpu_usage,\n",
        "        'file_size_kb': file_size_kb,\n",
        "        'is_hand_edited': int(is_hand_edited),\n",
        "        'ethical_concerns_flag': int(ethical_concerns_flag),\n",
        "        'width': width,\n",
        "        'height': height,\n",
        "        'day_of_week': day_of_week,\n",
        "        'month': month,\n",
        "        'hour': hour\n",
        "    }\n",
        "\n",
        "    # --- 2. Perform the same feature engineering as in training ---\n",
        "    # Basic derived features\n",
        "    sample_data['aspect_ratio'] = width / height if height > 0 else 0\n",
        "    sample_data['total_pixels'] = width * height\n",
        "    sample_data['is_square'] = int(width == height)\n",
        "    sample_data['is_weekend'] = int(day_of_week >= 5)\n",
        "\n",
        "    # One-hot encode platform\n",
        "    for p in ['Twitter', 'TikTok', 'Reddit', 'Instagram']:\n",
        "        sample_data[f'platform_{p}'] = 1 if platform == p else 0\n",
        "\n",
        "    # Advanced interaction features\n",
        "    sample_data['engagement_rate'] = likes / (sample_data['total_pixels'] / 1000000 + 1)\n",
        "    sample_data['quality_engagement'] = style_accuracy_score * likes / 100\n",
        "    sample_data['file_density'] = file_size_kb / (sample_data['total_pixels'] / 1000 + 1)\n",
        "    sample_data['gpu_efficiency'] = generation_time / (gpu_usage + 1)\n",
        "\n",
        "    # Platform-specific likes\n",
        "    for p in ['Twitter', 'TikTok', 'Reddit', 'Instagram']:\n",
        "         sample_data[f'{p.lower()}_likes'] = likes * sample_data[f'platform_{p}']\n",
        "\n",
        "    # Temporal cyclical features\n",
        "    sample_data['month_sin'] = np.sin(2 * np.pi * month / 12)\n",
        "    sample_data['month_cos'] = np.cos(2 * np.pi * month / 12)\n",
        "    sample_data['day_sin'] = np.sin(2 * np.pi * day_of_week / 7)\n",
        "    sample_data['day_cos'] = np.cos(2 * np.pi * day_of_week / 7)\n",
        "\n",
        "    # --- 3. Align columns with the training data ---\n",
        "    # Create a DataFrame and ensure it has the exact same columns in the same order as the training data\n",
        "    sample_df = pd.DataFrame([sample_data])\n",
        "    sample_df = sample_df.reindex(columns=expected_columns, fill_value=0)\n",
        "\n",
        "    # --- 4. Scale the features ---\n",
        "    try:\n",
        "        sample_scaled = scaler.transform(sample_df)\n",
        "    except Exception as e:\n",
        "        return 0, f\"Error during scaling: {e}\"\n",
        "\n",
        "    # --- 5. Predict with all models and format output ---\n",
        "    predictions = {}\n",
        "    for name, model in all_models.items():\n",
        "        pred_value = model.predict(sample_scaled)[0]\n",
        "        predictions[name] = max(0, int(pred_value)) # Ensure non-negative and integer\n",
        "\n",
        "    return predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3qft9Mfu415x",
        "outputId": "7c6925a7-ce54-4dd4-abb0-fe7a5f395d4e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "==================================================\n",
            "         RUNNING TEST WITH SAMPLE INPUT\n",
            "==================================================\n",
            "\n",
            "--- Input Values ---\n",
            "                    likes: 850\n",
            "          generation_time: 7.5\n",
            "                gpu_usage: 88\n",
            "             file_size_kb: 1200\n",
            "                    width: 1080\n",
            "                   height: 1350\n",
            "     style_accuracy_score: 92\n",
            "           is_hand_edited: True\n",
            "    ethical_concerns_flag: False\n",
            "              day_of_week: 4\n",
            "                    month: 6\n",
            "                     hour: 19\n",
            "                 platform: Instagram\n",
            "\n",
            "--- Model Predictions ---\n",
            "               Model  Predicted Shares\n",
            "0  Gradient Boosting              1314\n",
            "1      Random Forest              1232\n",
            "2   Lasso Regression              1180\n",
            "3  Linear Regression              1175\n",
            "4   Ridge Regression              1169\n",
            "\n",
            "✅ Test complete!\n"
          ]
        }
      ],
      "source": [
        "# Only proceed if the models were loaded correctly\n",
        "if models_loaded:\n",
        "    print(\"\\n\" + \"=\"*50)\n",
        "    print(\"         RUNNING TEST WITH SAMPLE INPUT\")\n",
        "    print(\"=\"*50)\n",
        "\n",
        "    # 1. Define a dictionary with sample values for a hypothetical image\n",
        "    test_input = {\n",
        "        \"likes\": 850,\n",
        "        \"generation_time\": 7.5,\n",
        "        \"gpu_usage\": 88,\n",
        "        \"file_size_kb\": 1200,\n",
        "        \"width\": 1080,\n",
        "        \"height\": 1350,  # Portrait aspect ratio\n",
        "        \"style_accuracy_score\": 92,\n",
        "        \"is_hand_edited\": True,\n",
        "        \"ethical_concerns_flag\": False,\n",
        "        \"day_of_week\": 4,  # Friday\n",
        "        \"month\": 6,        # June\n",
        "        \"hour\": 19,        # 7 PM\n",
        "        \"platform\": \"Instagram\"\n",
        "    }\n",
        "\n",
        "    # 2. Call your function using the test input dictionary\n",
        "    # The ** operator unpacks the dictionary into keyword arguments\n",
        "    all_predictions = predict_shares_all_models(**test_input)\n",
        "\n",
        "    # 3. Display the results in a clean, readable format\n",
        "    print(\"\\n--- Input Values ---\")\n",
        "    for key, value in test_input.items():\n",
        "        print(f\"{key:>25}: {value}\")\n",
        "\n",
        "    print(\"\\n--- Model Predictions ---\")\n",
        "    # Convert the results to a pandas DataFrame for nice printing\n",
        "    results_df = pd.DataFrame(list(all_predictions.items()), columns=['Model', 'Predicted Shares'])\n",
        "    results_df = results_df.sort_values('Predicted Shares', ascending=False).reset_index(drop=True)\n",
        "\n",
        "    print(results_df.to_string())\n",
        "\n",
        "    print(\"\\n✅ Test complete!\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONkgTSKAbOZu"
      },
      "source": [
        "# Export Results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pehur_uDbPsF",
        "outputId": "e91160db-89b1-4b42-de22-415da079c50c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Results exported successfully!\n",
            "\n",
            "Files created:\n",
            "- regression_analysis_results.json\n",
            "- model_comparison.csv\n"
          ]
        }
      ],
      "source": [
        "# Save all results for presentation\n",
        "import json\n",
        "\n",
        "# Prepare results dictionary\n",
        "presentation_results = {\n",
        "    'dataset_info': {\n",
        "        'total_samples': len(df),\n",
        "        'features': X.shape[1],\n",
        "        'target_mean': float(y.mean()),\n",
        "        'target_median': float(y.median()),\n",
        "        'target_std': float(y.std())\n",
        "    },\n",
        "    'model_comparison': comparison_df.to_dict('records'),\n",
        "    'feature_correlations': correlations.head(10).to_dict('records')\n",
        "}\n",
        "\n",
        "# Save to JSON\n",
        "with open('results/regression_analysis_results.json', 'w') as f:\n",
        "    json.dump(presentation_results, f, indent=2)\n",
        "\n",
        "# Save comparison table as CSV\n",
        "comparison_df.to_csv('results/model_comparison.csv', index=False)\n",
        "\n",
        "print(\"Results exported successfully!\")\n",
        "print(\"\\nFiles created:\")\n",
        "print(\"- regression_analysis_results.json\")\n",
        "print(\"- model_comparison.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gS9BH1XudOXW"
      },
      "source": [
        "# Gradio Demo App"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "id": "xVXYa7sNaxF8",
        "outputId": "5b726664-dbd2-4206-ead7-782886664901"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "✅ All models and scaler loaded successfully!\n",
            "Models expect 29 features.\n",
            "* Running on local URL:  http://127.0.0.1:7860\n",
            "* Running on public URL: https://c56406accffdd945a3.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"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div><iframe src=\"https://c56406accffdd945a3.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/pandas/compat/_optional.py\", line 135, in import_optional_dependency\n",
            "    module = importlib.import_module(name)\n",
            "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/lib/python3.12/importlib/__init__.py\", line 90, in import_module\n",
            "    return _bootstrap._gcd_import(name[level:], package, level)\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"<frozen importlib._bootstrap>\", line 1387, in _gcd_import\n",
            "  File \"<frozen importlib._bootstrap>\", line 1360, in _find_and_load\n",
            "  File \"<frozen importlib._bootstrap>\", line 1324, in _find_and_load_unlocked\n",
            "ModuleNotFoundError: No module named 'tabulate'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/gradio/queueing.py\", line 625, in process_events\n",
            "    response = await route_utils.call_process_api(\n",
            "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/gradio/route_utils.py\", line 322, in call_process_api\n",
            "    output = await app.get_blocks().process_api(\n",
            "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/gradio/blocks.py\", line 2218, in process_api\n",
            "    result = await self.call_function(\n",
            "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/gradio/blocks.py\", line 1729, in call_function\n",
            "    prediction = await anyio.to_thread.run_sync(  # type: ignore\n",
            "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/anyio/to_thread.py\", line 56, in run_sync\n",
            "    return await get_async_backend().run_sync_in_worker_thread(\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/anyio/_backends/_asyncio.py\", line 2470, in run_sync_in_worker_thread\n",
            "    return await future\n",
            "           ^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/anyio/_backends/_asyncio.py\", line 967, in run\n",
            "    result = context.run(func, *args)\n",
            "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/gradio/utils.py\", line 894, in wrapper\n",
            "    response = f(*args, **kwargs)\n",
            "               ^^^^^^^^^^^^^^^^^^\n",
            "  File \"/tmp/ipykernel_70924/2141717853.py\", line 123, in predict_shares_all_models\n",
            "    all_models_table = all_results_df.to_markdown(index=False)\n",
            "                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/pandas/util/_decorators.py\", line 333, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "           ^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 2988, in to_markdown\n",
            "    tabulate = import_optional_dependency(\"tabulate\")\n",
            "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/pandas/compat/_optional.py\", line 138, in import_optional_dependency\n",
            "    raise ImportError(msg)\n",
            "ImportError: Missing optional dependency 'tabulate'.  Use pip or conda to install tabulate.\n",
            "Traceback (most recent call last):\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/pandas/compat/_optional.py\", line 135, in import_optional_dependency\n",
            "    module = importlib.import_module(name)\n",
            "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/usr/lib/python3.12/importlib/__init__.py\", line 90, in import_module\n",
            "    return _bootstrap._gcd_import(name[level:], package, level)\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"<frozen importlib._bootstrap>\", line 1387, in _gcd_import\n",
            "  File \"<frozen importlib._bootstrap>\", line 1360, in _find_and_load\n",
            "  File \"<frozen importlib._bootstrap>\", line 1324, in _find_and_load_unlocked\n",
            "ModuleNotFoundError: No module named 'tabulate'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/gradio/queueing.py\", line 625, in process_events\n",
            "    response = await route_utils.call_process_api(\n",
            "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/gradio/route_utils.py\", line 322, in call_process_api\n",
            "    output = await app.get_blocks().process_api(\n",
            "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/gradio/blocks.py\", line 2218, in process_api\n",
            "    result = await self.call_function(\n",
            "             ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/gradio/blocks.py\", line 1729, in call_function\n",
            "    prediction = await anyio.to_thread.run_sync(  # type: ignore\n",
            "                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/anyio/to_thread.py\", line 56, in run_sync\n",
            "    return await get_async_backend().run_sync_in_worker_thread(\n",
            "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/anyio/_backends/_asyncio.py\", line 2470, in run_sync_in_worker_thread\n",
            "    return await future\n",
            "           ^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/anyio/_backends/_asyncio.py\", line 967, in run\n",
            "    result = context.run(func, *args)\n",
            "             ^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/gradio/utils.py\", line 894, in wrapper\n",
            "    response = f(*args, **kwargs)\n",
            "               ^^^^^^^^^^^^^^^^^^\n",
            "  File \"/tmp/ipykernel_70924/2141717853.py\", line 123, in predict_shares_all_models\n",
            "    all_models_table = all_results_df.to_markdown(index=False)\n",
            "                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/pandas/util/_decorators.py\", line 333, in wrapper\n",
            "    return func(*args, **kwargs)\n",
            "           ^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/pandas/core/frame.py\", line 2988, in to_markdown\n",
            "    tabulate = import_optional_dependency(\"tabulate\")\n",
            "               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
            "  File \"/home/ssyok/Documents/UM/GFW0003 Data Analytics /Group Assignment/AI-Ghibli-Image-Virality-Predictor/venv/lib/python3.12/site-packages/pandas/compat/_optional.py\", line 138, in import_optional_dependency\n",
            "    raise ImportError(msg)\n",
            "ImportError: Missing optional dependency 'tabulate'.  Use pip or conda to install tabulate.\n"
          ]
        }
      ],
      "source": [
        "import gradio as gr\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import joblib\n",
        "import os\n",
        "\n",
        "# ==============================================================================\n",
        "# 1. LOAD MODELS AND SCALER (This part runs once when the script starts)\n",
        "# ==============================================================================\n",
        "\n",
        "# Dictionary to hold the loaded model objects and a list of their names\n",
        "all_models = {}\n",
        "model_names = [\n",
        "    'Linear Regression', 'Ridge Regression', 'Lasso Regression',\n",
        "    'Random Forest', 'Gradient Boosting'\n",
        "]\n",
        "BEST_MODEL_NAME = 'Random Forest' # Define the best model to be highlighted\n",
        "\n",
        "try:\n",
        "    # Load all the regression models\n",
        "    for name in model_names:\n",
        "        # Construct the filename, e.g., 'models/random_forest.joblib'\n",
        "        filename = f\"models/{name.lower().replace(' ', '_')}.joblib\"\n",
        "        if os.path.exists(filename):\n",
        "            all_models[name] = joblib.load(filename)\n",
        "        else:\n",
        "            raise FileNotFoundError(f\"Model file not found: {filename}\")\n",
        "\n",
        "    # Load the scaler\n",
        "    scaler_path = 'models/scaler.joblib'\n",
        "    if os.path.exists(scaler_path):\n",
        "        scaler = joblib.load(scaler_path)\n",
        "    else:\n",
        "        raise FileNotFoundError(f\"Scaler file not found: {scaler_path}\")\n",
        "\n",
        "    models_loaded = True\n",
        "    print(\"✅ All models and scaler loaded successfully!\")\n",
        "\n",
        "    # Get the feature names the model was trained on from the scaler\n",
        "    expected_columns = scaler.feature_names_in_\n",
        "    print(f\"Models expect {len(expected_columns)} features.\")\n",
        "\n",
        "except Exception as e:\n",
        "    print(f\"❌ ERROR: Could not load models. {e}\")\n",
        "    print(\"Please ensure all '.joblib' files are in the 'models/' directory.\")\n",
        "    models_loaded = False\n",
        "    all_models = {}\n",
        "    scaler = None\n",
        "    expected_columns = []\n",
        "\n",
        "# ==============================================================================\n",
        "# 2. PREDICTION FUNCTION\n",
        "# ==============================================================================\n",
        "\n",
        "def predict_shares_all_models(likes, generation_time, gpu_usage, file_size_kb,\n",
        "                              width, height, style_accuracy_score,\n",
        "                              is_hand_edited, ethical_concerns_flag,\n",
        "                              day_of_week, month, hour, platform):\n",
        "    \"\"\"\n",
        "    Performs feature engineering, predicts shares using all loaded models,\n",
        "    and returns formatted outputs for the Gradio interface.\n",
        "    \"\"\"\n",
        "    if not models_loaded:\n",
        "        error_message = \"Models are not loaded. Please check the console for errors.\"\n",
        "        return 0, error_message, error_message\n",
        "\n",
        "    # --- Step A: Perform feature engineering ---\n",
        "    sample_data = {\n",
        "        'likes': likes,\n",
        "        'style_accuracy_score': style_accuracy_score,\n",
        "        'generation_time': generation_time,\n",
        "        'gpu_usage': gpu_usage,\n",
        "        'file_size_kb': file_size_kb,\n",
        "        'is_hand_edited': int(is_hand_edited),\n",
        "        'ethical_concerns_flag': int(ethical_concerns_flag),\n",
        "        'width': width,\n",
        "        'height': height,\n",
        "        'day_of_week': day_of_week,\n",
        "        'month': month,\n",
        "        'hour': hour\n",
        "    }\n",
        "\n",
        "    sample_data['aspect_ratio'] = width / height if height > 0 else 0\n",
        "    sample_data['total_pixels'] = width * height\n",
        "    sample_data['is_square'] = int(width == height)\n",
        "    sample_data['is_weekend'] = int(day_of_week >= 5)\n",
        "\n",
        "    for p in ['Twitter', 'TikTok', 'Reddit', 'Instagram']:\n",
        "        sample_data[f'platform_{p}'] = 1 if platform == p else 0\n",
        "\n",
        "    sample_data['engagement_rate'] = likes / (sample_data['total_pixels'] / 1000000 + 1)\n",
        "    sample_data['quality_engagement'] = style_accuracy_score * likes / 100\n",
        "    sample_data['file_density'] = file_size_kb / (sample_data['total_pixels'] / 1000 + 1)\n",
        "    sample_data['gpu_efficiency'] = generation_time / (gpu_usage + 1)\n",
        "\n",
        "    for p in ['Twitter', 'TikTok', 'Reddit', 'Instagram']:\n",
        "         sample_data[f'{p.lower()}_likes'] = likes * sample_data[f'platform_{p}']\n",
        "\n",
        "    sample_data['month_sin'] = np.sin(2 * np.pi * month / 12)\n",
        "    sample_data['month_cos'] = np.cos(2 * np.pi * month / 12)\n",
        "    sample_data['day_sin'] = np.sin(2 * np.pi * day_of_week / 7)\n",
        "    sample_data['day_cos'] = np.cos(2 * np.pi * day_of_week / 7)\n",
        "\n",
        "    # --- Step B: Align columns and Scale ---\n",
        "    sample_df = pd.DataFrame([sample_data])\n",
        "    sample_df = sample_df.reindex(columns=expected_columns, fill_value=0)\n",
        "    sample_scaled = scaler.transform(sample_df)\n",
        "\n",
        "    # --- Step C: Predict with all models ---\n",
        "    predictions = {}\n",
        "    for name, model in all_models.items():\n",
        "        pred_value = model.predict(sample_scaled)[0]\n",
        "        predictions[name] = max(0, int(pred_value))\n",
        "\n",
        "    # --- Step D: Format the outputs for Gradio ---\n",
        "\n",
        "    # 1. Get the single best model prediction\n",
        "    best_model_prediction = predictions.get(BEST_MODEL_NAME, 0)\n",
        "\n",
        "    # 2. Create a Markdown table for all model predictions\n",
        "    all_results_df = pd.DataFrame(list(predictions.items()), columns=['Model', 'Predicted Shares'])\n",
        "    all_results_df = all_results_df.sort_values('Predicted Shares', ascending=False)\n",
        "    all_models_table = all_results_df.to_markdown(index=False)\n",
        "\n",
        "    # 3. Create a Markdown table for the engineered features\n",
        "    features_df = sample_df.T.reset_index()\n",
        "    features_df.columns = ['Feature', 'Value']\n",
        "    features_df['Value'] = features_df['Value'].apply(lambda x: f\"{x:.4f}\" if isinstance(x, float) else x)\n",
        "    features_table = features_df.to_markdown(index=False)\n",
        "\n",
        "    return best_model_prediction, all_models_table, features_table\n",
        "\n",
        "# ==============================================================================\n",
        "# 3. GRADIO INTERFACE\n",
        "# ==============================================================================\n",
        "\n",
        "with gr.Blocks(theme=gr.themes.Soft(), title=\"AI Image Virality Predictor\") as demo:\n",
        "    gr.Markdown(\"# 🎨 AI Ghibli Image Virality Predictor\")\n",
        "    gr.Markdown(\"Enter image features to get a virality prediction from multiple regression models.\")\n",
        "\n",
        "    with gr.Row():\n",
        "        # --- INPUTS COLUMN ---\n",
        "        with gr.Column(scale=2):\n",
        "            gr.Markdown(\"### 1. Input Features\")\n",
        "            with gr.Accordion(\"Core Engagement & Image Metrics\", open=True):\n",
        "                likes = gr.Slider(minimum=0, maximum=10000, value=500, step=10, label=\"Likes\")\n",
        "                style_accuracy_score = gr.Slider(minimum=0, maximum=100, value=85, step=1, label=\"Style Accuracy Score (%)\")\n",
        "                width = gr.Slider(minimum=256, maximum=2048, value=1024, step=64, label=\"Width (px)\")\n",
        "                height = gr.Slider(minimum=256, maximum=2048, value=1024, step=64, label=\"Height (px)\")\n",
        "                file_size_kb = gr.Slider(minimum=100, maximum=5000, value=1500, step=100, label=\"File Size (KB)\")\n",
        "\n",
        "            with gr.Accordion(\"Technical & Posting Details\", open=True):\n",
        "                generation_time = gr.Slider(minimum=1, maximum=30, value=8, step=0.5, label=\"Generation Time (s)\")\n",
        "                gpu_usage = gr.Slider(minimum=10, maximum=100, value=70, step=5, label=\"GPU Usage (%)\")\n",
        "                platform = gr.Radio([\"Instagram\", \"Twitter\", \"TikTok\", \"Reddit\"], label=\"Platform\", value=\"Instagram\")\n",
        "                day_of_week = gr.Slider(minimum=0, maximum=6, value=4, step=1, label=\"Day of Week (0=Mon, 6=Sun)\")\n",
        "                month = gr.Slider(minimum=1, maximum=12, value=7, step=1, label=\"Month (1-12)\")\n",
        "                hour = gr.Slider(minimum=0, maximum=23, value=18, step=1, label=\"Hour of Day (0-23)\")\n",
        "                is_hand_edited = gr.Checkbox(label=\"Was it Hand Edited?\", value=False)\n",
        "                ethical_concerns_flag = gr.Checkbox(label=\"Any Ethical Concerns?\", value=False)\n",
        "\n",
        "            predict_btn = gr.Button(\"Predict Virality\", variant=\"primary\")\n",
        "\n",
        "        # --- OUTPUTS COLUMN ---\n",
        "        with gr.Column(scale=3):\n",
        "            gr.Markdown(\"### 2. Prediction Results\")\n",
        "\n",
        "            # Highlighted Best Model Output\n",
        "            best_model_output = gr.Number(\n",
        "                label=f\"🏆 Best Model Prediction ({BEST_MODEL_NAME})\",\n",
        "                interactive=False\n",
        "            )\n",
        "\n",
        "            # Table for All Model Predictions\n",
        "            with gr.Accordion(\"Comparison of All Models\", open=True):\n",
        "                all_models_output = gr.Markdown(label=\"All Model Predictions\")\n",
        "\n",
        "            # Table for Feature Engineering Details\n",
        "            with gr.Accordion(\"View Engineered Features\", open=False):\n",
        "                features_output = gr.Markdown(label=\"Feature Engineering Details\")\n",
        "\n",
        "    # Connect the button to the function\n",
        "    predict_btn.click(\n",
        "        fn=predict_shares_all_models,\n",
        "        inputs=[\n",
        "            likes, generation_time, gpu_usage, file_size_kb,\n",
        "            width, height, style_accuracy_score,\n",
        "            is_hand_edited, ethical_concerns_flag,\n",
        "            day_of_week, month, hour, platform\n",
        "        ],\n",
        "        outputs=[\n",
        "            best_model_output,\n",
        "            all_models_output,\n",
        "            features_output\n",
        "        ]\n",
        "    )\n",
        "\n",
        "# Launch the app\n",
        "if __name__ == \"__main__\":\n",
        "    if not models_loaded:\n",
        "        print(\"\\nCannot launch Gradio app because models failed to load.\")\n",
        "    else:\n",
        "        demo.launch(share=True)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}