svr/SVR_topics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "de77946a",
   "metadata": {},
   "source": [
    "This notebook uses several different models to predict popularity using likes/views as popularity calculation. Additionally, this notebook uses topics as a predictor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cf4b1003-fc4a-4bbd-b3bc-77138a59ec34",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import ast\n",
    "from collections import OrderedDict\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "888eb112",
   "metadata": {},
   "source": [
    "For this regression model, we are going to use the sentiment analysis dataset, which includes the TextBlob and VADER scores from the sentiment analysis algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "18604b14-96f3-4e8e-a17b-61511e029bc2",
   "metadata": {},
   "outputs": [
    {
     "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>Unnamed: 0.1</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>_id</th>\n",
       "      <th>duration</th>\n",
       "      <th>likes</th>\n",
       "      <th>views</th>\n",
       "      <th>tb_score</th>\n",
       "      <th>vd_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4983.000000</td>\n",
       "      <td>4983.000000</td>\n",
       "      <td>4983.000000</td>\n",
       "      <td>4983.000000</td>\n",
       "      <td>4.983000e+03</td>\n",
       "      <td>4.983000e+03</td>\n",
       "      <td>4983.000000</td>\n",
       "      <td>4983.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2732.942003</td>\n",
       "      <td>2732.942003</td>\n",
       "      <td>24648.154525</td>\n",
       "      <td>686.436885</td>\n",
       "      <td>7.051936e+04</td>\n",
       "      <td>2.370903e+06</td>\n",
       "      <td>0.108438</td>\n",
       "      <td>0.741949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1648.784090</td>\n",
       "      <td>1648.784090</td>\n",
       "      <td>29866.800844</td>\n",
       "      <td>385.181603</td>\n",
       "      <td>1.134161e+05</td>\n",
       "      <td>3.833013e+06</td>\n",
       "      <td>0.062795</td>\n",
       "      <td>0.627445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.260000e+02</td>\n",
       "      <td>4.209000e+03</td>\n",
       "      <td>-0.259643</td>\n",
       "      <td>-0.999900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1290.500000</td>\n",
       "      <td>1290.500000</td>\n",
       "      <td>1515.500000</td>\n",
       "      <td>339.000000</td>\n",
       "      <td>2.700000e+04</td>\n",
       "      <td>9.297965e+05</td>\n",
       "      <td>0.070541</td>\n",
       "      <td>0.980050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2654.000000</td>\n",
       "      <td>2654.000000</td>\n",
       "      <td>2854.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>4.500000e+04</td>\n",
       "      <td>1.517960e+06</td>\n",
       "      <td>0.108392</td>\n",
       "      <td>0.997700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4176.500000</td>\n",
       "      <td>4176.500000</td>\n",
       "      <td>48651.500000</td>\n",
       "      <td>930.000000</td>\n",
       "      <td>6.900000e+04</td>\n",
       "      <td>2.323995e+06</td>\n",
       "      <td>0.146548</td>\n",
       "      <td>0.999400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5630.000000</td>\n",
       "      <td>5630.000000</td>\n",
       "      <td>95714.000000</td>\n",
       "      <td>4154.000000</td>\n",
       "      <td>2.200000e+06</td>\n",
       "      <td>7.338914e+07</td>\n",
       "      <td>0.594444</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0.1   Unnamed: 0           _id     duration         likes  \\\n",
       "count   4983.000000  4983.000000   4983.000000  4983.000000  4.983000e+03   \n",
       "mean    2732.942003  2732.942003  24648.154525   686.436885  7.051936e+04   \n",
       "std     1648.784090  1648.784090  29866.800844   385.181603  1.134161e+05   \n",
       "min        0.000000     0.000000      1.000000    60.000000  1.260000e+02   \n",
       "25%     1290.500000  1290.500000   1515.500000   339.000000  2.700000e+04   \n",
       "50%     2654.000000  2654.000000   2854.000000   683.000000  4.500000e+04   \n",
       "75%     4176.500000  4176.500000  48651.500000   930.000000  6.900000e+04   \n",
       "max     5630.000000  5630.000000  95714.000000  4154.000000  2.200000e+06   \n",
       "\n",
       "              views     tb_score     vd_score  \n",
       "count  4.983000e+03  4983.000000  4983.000000  \n",
       "mean   2.370903e+06     0.108438     0.741949  \n",
       "std    3.833013e+06     0.062795     0.627445  \n",
       "min    4.209000e+03    -0.259643    -0.999900  \n",
       "25%    9.297965e+05     0.070541     0.980050  \n",
       "50%    1.517960e+06     0.108392     0.997700  \n",
       "75%    2.323995e+06     0.146548     0.999400  \n",
       "max    7.338914e+07     0.594444     1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../data/data_sentiment_analysis.csv\")\n",
    "#df = df[0:20]\n",
    "# print(len(df))\n",
    "df = df.dropna()\n",
    "# print(len(df))\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bfb492df-6f0d-4df0-9f30-37b96c68b1ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create popularity column by dividing likes and views\n",
    "df['likes_log'] = np.log(df['likes'])\n",
    "df['popularity'] = (df['likes']/df['views']) * 100\n",
    "\n",
    "# Let's view the dataset again\n",
    "df.head()\n",
    "df['topics_list'] = df['topics_list'].apply(ast.literal_eval)\n",
    "# print(type(df['topics_list'][0]))\n",
    "#print(type(df['topics'][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aeab6319",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histogram\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(df['popularity'], bins=30, color='blue', alpha=0.7)\n",
    "plt.title('Histogram of Popularity')\n",
    "plt.xlabel('Popularity')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5b800780",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histogram\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(df['likes_log'], bins=30, color='blue', alpha=0.7)\n",
    "plt.title('Histogram of Log(Likes)')\n",
    "plt.xlabel('Log(Likes)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5c6c740",
   "metadata": {},
   "source": [
    "Count instances of each topic from the topics list column, and only keep topics that appear >100 times in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9c2c259",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "topics = [thing for inner_list in df['topics_list'].tolist() for thing in inner_list]\n",
    "count_topics = Counter(topics)\n",
    "count_dict = dict(count_topics)\n",
    "\n",
    "# print(count_dict)\n",
    "\n",
    "# topics with less than 100 instances\n",
    "under_100 = {k:v for (k,v) in count_dict.items() if v < 100}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04ccbca3",
   "metadata": {},
   "source": [
    "Print list of all the topics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2d13bcbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['3D printing' 'AI' 'AIDS' 'Africa' \"Alzheimer's\" 'Antarctica'\n",
      " 'Anthropocene' 'Asia' 'Audacious Project' 'Autism spectrum disorder'\n",
      " 'Best of the Web' 'Big Bang' 'Brazil' 'Buddhism' 'CRISPR' 'Christianity'\n",
      " 'Countdown' 'DNA' 'Egypt' 'Europe' 'Hinduism' 'Humanities' 'Internet'\n",
      " 'Islam' 'Judaism' 'LGBTQIA+' 'Life' 'Mars' 'Middle East' 'Mission Blue'\n",
      " 'Moon' 'NASA' 'NFTs' 'PTSD' 'Planets' 'Slavery' 'South America'\n",
      " 'String theory' 'Sun' 'Surgery' 'TED Books' 'TED Connects' 'TED Fellows'\n",
      " 'TED Membership' 'TED Prize' 'TED Residency' 'TED en Español' 'TED-Ed'\n",
      " 'TEDMED' 'TEDx' 'Transgender' 'UX design' 'United States' 'Vaccines'\n",
      " 'activism' 'addiction' 'aging' 'agriculture' 'algorithm' 'aliens'\n",
      " 'ancient world' 'animals' 'animation' 'anthropology' 'archaeology'\n",
      " 'architecture' 'art' 'asteroid' 'astrobiology' 'astronomy' 'atheism'\n",
      " 'augmented reality' 'bacteria' 'beauty' 'bees' 'behavioral economics'\n",
      " 'biodiversity' 'bioethics' 'biology' 'biomimicry' 'bionics' 'biosphere'\n",
      " 'biotech' 'birds' 'blindness' 'blockchain' 'body language' 'books'\n",
      " 'botany' 'brain' 'bullying' 'business' 'cancer' 'capitalism' 'chemistry'\n",
      " 'china' 'cities' 'climate change' 'code' 'cognitive science'\n",
      " 'collaboration' 'comedy' 'communication' 'community' 'compassion'\n",
      " 'computers' 'conducting' 'consciousness' 'conservation' 'consumerism'\n",
      " 'coral reefs' 'coronavirus' 'corruption' 'creativity' 'crime'\n",
      " 'crowdsourcing' 'cryptocurrency' 'culture' 'curiosity' 'cyber security'\n",
      " 'dance' 'dark matter' 'data' 'death' 'decision-making' 'deextinction'\n",
      " 'demo' 'democracy' 'depression' 'design' 'dinosaurs' 'disability'\n",
      " 'discovery' 'disease' 'diversity' 'driverless cars' 'drones' 'drugs'\n",
      " 'ebola' 'ecology' 'economics' 'education' 'electricity' 'emotions'\n",
      " 'empathy' 'encryption' 'energy' 'engineering' 'entertainment'\n",
      " 'entrepreneur' 'environment' 'equality' 'ethics' 'evolution' 'exercise'\n",
      " 'exploration' 'family' 'farming' 'fashion' 'fear' 'feminism' 'film'\n",
      " 'finance' 'fish' 'flight' 'food' 'forensics' 'fossil fuels' 'friendship'\n",
      " 'fungi' 'future' 'gaming' 'gardening' 'gender' 'genetics' 'geology'\n",
      " 'glaciers' 'global issues' 'goals' 'government' 'grammar'\n",
      " 'graphic design' 'happiness' 'health' 'health care' 'hearing' 'heart'\n",
      " 'history' 'homelessness' 'human body' 'human rights' 'humanity' 'humor'\n",
      " 'identity' 'illness' 'illusion' 'immigration' 'inclusion' 'india'\n",
      " 'indigenous peoples' 'industrial design' 'infrastructure' 'innovation'\n",
      " 'insects' 'international development' 'international relations'\n",
      " 'interview' 'invention' 'investing' 'journalism' 'justice system' 'kids'\n",
      " 'language' 'law' 'leadership' 'library' 'literature' 'love'\n",
      " 'machine learning' 'magic' 'manufacturing' 'maps' 'marine biology'\n",
      " 'marketing' 'math' 'media' 'medical imaging' 'medical research'\n",
      " 'medicine' 'meditation' 'memory' 'mental health' 'metaverse' 'microbes'\n",
      " 'microbiology' 'military' 'mindfulness' 'money' 'motivation' 'museums'\n",
      " 'music' 'nanotechnology' 'natural disaster' 'natural resources' 'nature'\n",
      " 'neurology' 'neuroscience' 'nuclear energy' 'ocean' 'online privacy'\n",
      " 'pain' 'painting' 'paleontology' 'pandemic' 'parenting' 'performance'\n",
      " 'personal growth' 'personality' 'philanthropy' 'philosophy' 'photography'\n",
      " 'physics' 'plants' 'plastic' 'poetry' 'policy' 'politics' 'pollution'\n",
      " 'potential' 'poverty' 'pregnancy' 'primates' 'prison' 'product design'\n",
      " 'productivity' 'prosthetics' 'protest' 'psychology' 'public health'\n",
      " 'public space' 'public speaking' 'quantum' 'race' 'refugees'\n",
      " 'relationships' 'religion' 'renewable energy' 'resources' 'rivers'\n",
      " 'robots' 'rocket science' 'science' 'science fiction' 'self' 'sex'\n",
      " 'sexual violence' 'shopping' 'sight' 'sleep' 'smell' 'social change'\n",
      " 'social media' 'society' 'sociology' 'software' 'solar energy'\n",
      " 'solar system' 'sound' 'space' 'spoken word' 'sports' 'statistics'\n",
      " 'storytelling' 'street art' 'success' 'suicide' 'surveillance'\n",
      " 'sustainability' 'synthetic biology' 'teaching' 'technology' 'telescopes'\n",
      " 'television' 'terrorism' 'theater' 'time' 'toys' 'transportation'\n",
      " 'travel' 'trees' 'trust' 'typography' 'universe' 'urban planning'\n",
      " 'veganism' 'violence' 'virtual reality' 'virus' 'visualizations'\n",
      " 'vulnerability' 'war' 'water' 'weather' 'wind energy' 'women'\n",
      " 'women in business' 'work' 'work-life balance' 'worklife' 'writing'\n",
      " 'youth']\n"
     ]
    }
   ],
   "source": [
    "mlb = MultiLabelBinarizer()\n",
    "mlb.fit_transform(df['topics_list'])\n",
    "print(mlb.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "22b8f914",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "351\n"
     ]
    }
   ],
   "source": [
    "# there are 351 different topics\n",
    "print(len(mlb.classes_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0b0f6285-ad50-4c48-b3bb-11a26d76f01a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>What if the commonly accepted narratives about...</td>\n",
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       "      <td>7200</td>\n",
       "      <td>[{'name': 'Dan Widmaier', 'occupation': 'Bioma...</td>\n",
       "      <td>[{'name': 'English', 'code': 'en'}]</td>\n",
       "      <td>Your closet is likely full of all kinds of mat...</td>\n",
       "      <td>[{'id': '5', 'name': 'environment'}, {'id': '8...</td>\n",
       "      <td>The future of fashion -- made from mushrooms</td>\n",
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       "      <td>4500</td>\n",
       "      <td>[{'name': 'Scott Fitsimones', 'occupation': 'E...</td>\n",
       "      <td>[{'name': 'English', 'code': 'en'}]</td>\n",
       "      <td>Could DAOs, or \"decentralized autonomous organ...</td>\n",
       "      <td>[{'id': '45', 'name': 'cities'}, {'id': '51', ...</td>\n",
       "      <td>Could a DAO build the next great city?</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4983 rows × 372 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0.1  Unnamed: 0    _id  duration   likes  \\\n",
       "0                0           0     21       992   17000   \n",
       "1                1           1      1       957  110000   \n",
       "2                2           2      7      1266   60000   \n",
       "3                3           3     47      1126   80000   \n",
       "4                4           4     55      1524   14000   \n",
       "...            ...         ...    ...       ...     ...   \n",
       "5626          5626        5626  95714       278    5800   \n",
       "5627          5627        5627  95710       940   10000   \n",
       "5628          5628        5628  95451      1048   12000   \n",
       "5629          5629        5629  95708       651    7200   \n",
       "5630          5630        5630  95709       649    4500   \n",
       "\n",
       "                                               speakers  \\\n",
       "0     [{'name': 'Mena Trott', 'occupation': 'Blogger...   \n",
       "1     [{'name': 'Al Gore', 'occupation': 'Climate ad...   \n",
       "2     [{'name': 'David Pogue', 'occupation': 'Techno...   \n",
       "3     [{'name': 'David Deutsch', 'occupation': 'Phys...   \n",
       "4     [{'name': 'Jehane Noujaim', 'occupation': 'Fil...   \n",
       "...                                                 ...   \n",
       "5626  [{'name': 'Lyla Latif', 'occupation': 'Educato...   \n",
       "5627  [{'name': 'Trish Millines Dziko', 'occupation'...   \n",
       "5628  [{'name': 'David Wengrow', 'occupation': 'Arch...   \n",
       "5629  [{'name': 'Dan Widmaier', 'occupation': 'Bioma...   \n",
       "5630  [{'name': 'Scott Fitsimones', 'occupation': 'E...   \n",
       "\n",
       "                                     subtitle_languages  \\\n",
       "0     [{'name': 'English', 'code': 'en'}, {'name': '...   \n",
       "1     [{'name': 'Greek', 'code': 'el'}, {'name': 'Gu...   \n",
       "2     [{'name': 'Greek', 'code': 'el'}, {'name': 'En...   \n",
       "3     [{'name': 'Greek', 'code': 'el'}, {'name': 'En...   \n",
       "4     [{'name': 'English', 'code': 'en'}, {'name': '...   \n",
       "...                                                 ...   \n",
       "5626  [{'name': 'English', 'code': 'en'}, {'name': '...   \n",
       "5627  [{'name': 'English', 'code': 'en'}, {'name': '...   \n",
       "5628  [{'name': 'English', 'code': 'en'}, {'name': '...   \n",
       "5629                [{'name': 'English', 'code': 'en'}]   \n",
       "5630                [{'name': 'English', 'code': 'en'}]   \n",
       "\n",
       "                                                summary  \\\n",
       "0     The founding mother of the blog revolution, Mo...   \n",
       "1     With the same humor and humanity he exuded in ...   \n",
       "2     New York Times columnist David Pogue takes aim...   \n",
       "3     Legendary scientist David Deutsch puts theoret...   \n",
       "4     Jehane Noujaim unveils her 2006 TED Prize wish...   \n",
       "...                                                 ...   \n",
       "5626  In 2020, Mali produced over 71 tons of gold— a...   \n",
       "5627  Forget home economics and standardized tests, ...   \n",
       "5628  What if the commonly accepted narratives about...   \n",
       "5629  Your closet is likely full of all kinds of mat...   \n",
       "5630  Could DAOs, or \"decentralized autonomous organ...   \n",
       "\n",
       "                                                 topics  \\\n",
       "0     [{'id': '4', 'name': 'culture'}, {'id': '13', ...   \n",
       "1     [{'id': '3', 'name': 'climate change'}, {'id':...   \n",
       "2     [{'id': '10', 'name': 'technology'}, {'id': '3...   \n",
       "3     [{'id': '3', 'name': 'climate change'}, {'id':...   \n",
       "4     [{'id': '4', 'name': 'culture'}, {'id': '6', '...   \n",
       "...                                                 ...   \n",
       "5626  [{'id': '18', 'name': 'business'}, {'id': '19'...   \n",
       "5627  [{'id': '52', 'name': 'education'}, {'id': '53...   \n",
       "5628  [{'id': '4', 'name': 'culture'}, {'id': '81', ...   \n",
       "5629  [{'id': '5', 'name': 'environment'}, {'id': '8...   \n",
       "5630  [{'id': '45', 'name': 'cities'}, {'id': '51', ...   \n",
       "\n",
       "                                                  title  ... water  weather  \\\n",
       "0               Meet the founder of the blog revolution  ...     0        0   \n",
       "1                           Averting the climate crisis  ...     0        0   \n",
       "2                                      Simplicity sells  ...     0        0   \n",
       "3           Chemical scum that dream of distant quasars  ...     0        0   \n",
       "4                         My wish: A global day of film  ...     0        0   \n",
       "...                                                 ...  ...   ...      ...   \n",
       "5626                              The true cost of gold  ...     0        0   \n",
       "5627     How schools can nurture every student's genius  ...     0        0   \n",
       "5628  A new understanding of human history and the r...  ...     0        0   \n",
       "5629       The future of fashion -- made from mushrooms  ...     0        0   \n",
       "5630             Could a DAO build the next great city?  ...     0        0   \n",
       "\n",
       "     wind energy women women in business work work-life balance  worklife  \\\n",
       "0              0     0                 0    0                 0         0   \n",
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       "2              0     0                 0    0                 0         0   \n",
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       "4              0     0                 0    0                 0         0   \n",
       "...          ...   ...               ...  ...               ...       ...   \n",
       "5626           0     0                 0    0                 0         0   \n",
       "5627           0     0                 0    0                 0         0   \n",
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       "5629           0     0                 0    0                 0         0   \n",
       "5630           0     0                 0    0                 0         0   \n",
       "\n",
       "      writing  youth  \n",
       "0           0      0  \n",
       "1           0      0  \n",
       "2           0      0  \n",
       "3           0      0  \n",
       "4           0      0  \n",
       "...       ...    ...  \n",
       "5626        0      0  \n",
       "5627        0      1  \n",
       "5628        0      0  \n",
       "5629        0      0  \n",
       "5630        0      0  \n",
       "\n",
       "[4983 rows x 372 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one hot encode the topics in the topics list\n",
    "topics_encoded = pd.DataFrame(mlb.fit_transform(df['topics_list']), columns=mlb.classes_, index=df.index)\n",
    "df_encoded = pd.concat([df, topics_encoded], axis=1).drop('topics_list', axis=1)\n",
    "df_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1438db10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get rid of the topics with too few instances\n",
    "\n",
    "df_encoded = df_encoded.drop(columns=under_100.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "17ad2861-984b-4aa6-83d5-43a6861b6f31",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 'Unnamed: 0',\n",
       " '_id',\n",
       " 'duration',\n",
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       " 'speakers',\n",
       " 'subtitle_languages',\n",
       " 'summary',\n",
       " 'topics',\n",
       " 'title',\n",
       " 'transcript',\n",
       " 'views',\n",
       " 'recorded_date',\n",
       " 'published_date',\n",
       " 'speakers_list',\n",
       " 'sl_list',\n",
       " 'processed_transcript',\n",
       " 'tb_score',\n",
       " 'vd_score',\n",
       " 'likes_log',\n",
       " 'popularity',\n",
       " 'Africa',\n",
       " 'Countdown',\n",
       " 'Internet',\n",
       " 'TED Fellows',\n",
       " 'TED-Ed',\n",
       " 'TEDx',\n",
       " 'United States',\n",
       " 'activism',\n",
       " 'animals',\n",
       " 'animation',\n",
       " 'architecture',\n",
       " 'art',\n",
       " 'biology',\n",
       " 'brain',\n",
       " 'business',\n",
       " 'cities',\n",
       " 'climate change',\n",
       " 'collaboration',\n",
       " 'communication',\n",
       " 'community',\n",
       " 'computers',\n",
       " 'creativity',\n",
       " 'culture',\n",
       " 'data',\n",
       " 'decision-making',\n",
       " 'democracy',\n",
       " 'design',\n",
       " 'disease',\n",
       " 'economics',\n",
       " 'education',\n",
       " 'energy',\n",
       " 'engineering',\n",
       " 'entertainment',\n",
       " 'entrepreneur',\n",
       " 'environment',\n",
       " 'equality',\n",
       " 'evolution',\n",
       " 'exploration',\n",
       " 'family',\n",
       " 'food',\n",
       " 'future',\n",
       " 'gender',\n",
       " 'global issues',\n",
       " 'government',\n",
       " 'happiness',\n",
       " 'health',\n",
       " 'health care',\n",
       " 'history',\n",
       " 'human body',\n",
       " 'humanity',\n",
       " 'humor',\n",
       " 'identity',\n",
       " 'illness',\n",
       " 'innovation',\n",
       " 'invention',\n",
       " 'journalism',\n",
       " 'kids',\n",
       " 'language',\n",
       " 'law',\n",
       " 'leadership',\n",
       " 'math',\n",
       " 'media',\n",
       " 'medical research',\n",
       " 'medicine',\n",
       " 'mental health',\n",
       " 'motivation',\n",
       " 'music',\n",
       " 'nature',\n",
       " 'neuroscience',\n",
       " 'ocean',\n",
       " 'performance',\n",
       " 'personal growth',\n",
       " 'philosophy',\n",
       " 'photography',\n",
       " 'physics',\n",
       " 'policy',\n",
       " 'politics',\n",
       " 'potential',\n",
       " 'psychology',\n",
       " 'public health',\n",
       " 'race',\n",
       " 'relationships',\n",
       " 'religion',\n",
       " 'science',\n",
       " 'self',\n",
       " 'social change',\n",
       " 'society',\n",
       " 'space',\n",
       " 'storytelling',\n",
       " 'sustainability',\n",
       " 'technology',\n",
       " 'universe',\n",
       " 'violence',\n",
       " 'visualizations',\n",
       " 'war',\n",
       " 'women',\n",
       " 'work',\n",
       " 'writing']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view columns\n",
    "df_encoded.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9dcc361d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "119"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view length of variables\n",
    "len(df_encoded.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "34eefe36-b29a-4147-bf38-bbbea233929a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# eliminate columns from dataset that we do not need for model\n",
    "X = df_encoded.drop(['Unnamed: 0.1', 'Unnamed: 0', '_id', 'likes', 'published_date', 'recorded_date', 'speakers', 'subtitle_languages', 'summary', 'title', 'topics', 'transcript','views', 'speakers_list','sl_list','processed_transcript', 'popularity', 'likes_log'], axis=1)\n",
    "# assign the y variable to be popularity\n",
    "y = df_encoded['likes_log']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cd661b4c-801a-4308-bb09-897864393de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into testing and training\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "26c92b9d",
   "metadata": {},
   "outputs": [
    {
     "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>duration</th>\n",
       "      <th>tb_score</th>\n",
       "      <th>vd_score</th>\n",
       "      <th>Africa</th>\n",
       "      <th>Countdown</th>\n",
       "      <th>Internet</th>\n",
       "      <th>TED Fellows</th>\n",
       "      <th>TED-Ed</th>\n",
       "      <th>TEDx</th>\n",
       "      <th>United States</th>\n",
       "      <th>...</th>\n",
       "      <th>storytelling</th>\n",
       "      <th>sustainability</th>\n",
       "      <th>technology</th>\n",
       "      <th>universe</th>\n",
       "      <th>violence</th>\n",
       "      <th>visualizations</th>\n",
       "      <th>war</th>\n",
       "      <th>women</th>\n",
       "      <th>work</th>\n",
       "      <th>writing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>992</td>\n",
       "      <td>0.133855</td>\n",
       "      <td>0.9996</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>957</td>\n",
       "      <td>0.131762</td>\n",
       "      <td>0.9997</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1266</td>\n",
       "      <td>0.155493</td>\n",
       "      <td>0.9999</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1126</td>\n",
       "      <td>0.066814</td>\n",
       "      <td>0.9990</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1524</td>\n",
       "      <td>0.127882</td>\n",
       "      <td>0.9995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration  tb_score  vd_score  Africa  Countdown  Internet  TED Fellows  \\\n",
       "0       992  0.133855    0.9996       0          0         0            0   \n",
       "1       957  0.131762    0.9997       0          0         0            0   \n",
       "2      1266  0.155493    0.9999       0          0         0            0   \n",
       "3      1126  0.066814    0.9990       0          0         0            0   \n",
       "4      1524  0.127882    0.9995       0          0         0            0   \n",
       "\n",
       "   TED-Ed  TEDx  United States  ...  storytelling  sustainability  technology  \\\n",
       "0       0     0              0  ...             1               0           0   \n",
       "1       0     0              0  ...             0               1           1   \n",
       "2       0     0              0  ...             0               0           1   \n",
       "3       0     0              0  ...             0               0           1   \n",
       "4       0     0              0  ...             1               0           0   \n",
       "\n",
       "   universe  violence  visualizations  war  women  work  writing  \n",
       "0         0         0               0    0      0     0        0  \n",
       "1         0         0               0    0      0     0        0  \n",
       "2         0         0               0    0      0     0        0  \n",
       "3         1         0               0    0      0     0        0  \n",
       "4         0         0               0    0      0     0        0  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9015ea2e-d7ff-485b-a806-6b661a2c4d04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale data\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17486327",
   "metadata": {},
   "source": [
    "Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dbb4d232",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a65fe265",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14024677553022324"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fit model using scaled training data\n",
    "reg = linear_model.LinearRegression().fit(X_train_scaled, y_train)\n",
    "reg.score(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "08b0efea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8875797307140668"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict y value and calculate mse\n",
    "y_pred_linear = reg.predict(X_test_scaled)\n",
    "mse_linear = np.mean((y_pred_linear - y_test) ** 2)\n",
    "mse_linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6eede392",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8875797307140668\n",
      "0.09192294015401081\n"
     ]
    }
   ],
   "source": [
    "print(mean_squared_error(y_test, y_pred_linear))\n",
    "print(r2_score(y_test, y_pred_linear))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "374330c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Actual values from the DataFrame\n",
    "actual_values = y_test # Replace 'actual_values' with the actual column name\n",
    "\n",
    "# Predicted values from the DataFrame\n",
    "predicted_values = y_pred_linear  # Replace 'predicted_values' with the actual column name\n",
    "\n",
    "# Create a scatter plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(actual_values, predicted_values, color='blue', alpha=0.7, label='Predicted Values', marker='o')\n",
    "plt.scatter(actual_values, actual_values, color='red', alpha=0.7, label='Actual Values', marker='x')\n",
    "plt.title('Actual vs Predicted Values')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2af88d40",
   "metadata": {},
   "source": [
    "Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6eb8b946",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Lasso(alpha=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso(alpha=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Lasso(alpha=1)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg = linear_model.Lasso(alpha=1)\n",
    "reg.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7d72c612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.980582355210467"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict popularity using lasso regression\n",
    "y_pred_lasso = reg.predict(X_test_scaled)\n",
    "mse_lasso = np.mean((y_pred_lasso - y_test) ** 2)\n",
    "mse_lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2e4bb861",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.980582355210467\n",
      "-0.0032274411449266793\n"
     ]
    }
   ],
   "source": [
    "print(mean_squared_error(y_test, y_pred_lasso))\n",
    "print(r2_score(y_test, y_pred_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3803dd69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Actual values from the DataFrame\n",
    "actual_values = y_test # Replace 'actual_values' with the actual column name\n",
    "\n",
    "# Predicted values from the DataFrame\n",
    "predicted_values = y_pred_lasso  # Replace 'predicted_values' with the actual column name\n",
    "\n",
    "# Create a scatter plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(actual_values, predicted_values, color='blue', alpha=0.7, label='Predicted Values', marker='o')\n",
    "plt.scatter(actual_values, actual_values, color='red', alpha=0.7, label='Actual Values', marker='x')\n",
    "plt.title('Actual vs Predicted Values')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6324b79a",
   "metadata": {},
   "source": [
    "Support Vector Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a7d57109-b2e1-43d7-ada5-baba5e8b40f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "svr_linear = SVR(kernel='linear', C=1.0, epsilon=0.1)\n",
    "svr_rbf = SVR(kernel='rbf', C=1.0, epsilon=0.1)\n",
    "svr_poly = SVR(kernel='poly', degree=3, C=1.0, epsilon=0.1)\n",
    "svr_sigmoid = SVR(kernel='sigmoid', C=1.0, epsilon=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "532331f0-bfbc-4b4d-90f3-c523829ead0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR(kernel=&#x27;sigmoid&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR(kernel=&#x27;sigmoid&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVR(kernel='sigmoid')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr_linear.fit(X_train_scaled, y_train)\n",
    "svr_rbf.fit(X_train_scaled, y_train)\n",
    "svr_poly.fit(X_train_scaled, y_train)\n",
    "svr_sigmoid.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "42df17f8-e1b7-4026-8ee9-74eb944805e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_svr_linear = svr_linear.predict(X_test_scaled)\n",
    "y_pred_rbf = svr_rbf.predict(X_test_scaled)\n",
    "y_pred_poly = svr_poly.predict(X_test_scaled)\n",
    "y_pred_sigmoid = svr_sigmoid.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e2c1bc55-edb6-41ae-8bda-ebc60348552d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8833033341065389"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_linear = np.mean((y_pred_svr_linear - y_test) ** 2)\n",
    "mse_linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c58baeb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0962980937584933\n",
      "0.12765529900071382\n",
      "0.05297063322136342\n",
      "-3.7613152720799565\n"
     ]
    }
   ],
   "source": [
    "print(r2_score(y_test, y_pred_svr_linear))\n",
    "print(r2_score(y_test, y_pred_rbf))\n",
    "print(r2_score(y_test, y_pred_poly))\n",
    "print(r2_score(y_test, y_pred_sigmoid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "34444b6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Actual values from the DataFrame\n",
    "actual_values = y_test # Replace 'actual_values' with the actual column name\n",
    "\n",
    "# Predicted values from the DataFrame\n",
    "predicted_values = y_pred_svr_linear  # Replace 'predicted_values' with the actual column name\n",
    "\n",
    "# Create a scatter plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(actual_values, predicted_values, color='blue', alpha=0.7, label='Predicted Values', marker='o')\n",
    "plt.scatter(actual_values, actual_values, color='red', alpha=0.7, label='Actual Values', marker='x')\n",
    "plt.title('Actual vs Predicted Values')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f82c4fec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8526539310816942"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_rbf = np.mean((y_pred_rbf - y_test) ** 2)\n",
    "mse_rbf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6351ca27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Actual values from the DataFrame\n",
    "actual_values = y_test # Replace 'actual_values' with the actual column name\n",
    "\n",
    "# Predicted values from the DataFrame\n",
    "predicted_values = y_pred_rbf  # Replace 'predicted_values' with the actual column name\n",
    "\n",
    "# Create a scatter plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(actual_values, predicted_values, color='blue', alpha=0.7, label='Predicted Values', marker='o')\n",
    "plt.scatter(actual_values, actual_values, color='red', alpha=0.7, label='Actual Values', marker='x')\n",
    "plt.title('Actual vs Predicted Values')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3c621692",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9256527970062981"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_poly = np.mean((y_pred_poly - y_test) ** 2)\n",
    "mse_poly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c469f96e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Actual values from the DataFrame\n",
    "actual_values = y_test # Replace 'actual_values' with the actual column name\n",
    "\n",
    "# Predicted values from the DataFrame\n",
    "predicted_values = y_pred_poly  # Replace 'predicted_values' with the actual column name\n",
    "\n",
    "# Create a scatter plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(actual_values, predicted_values, color='blue', alpha=0.7, label='Predicted Values', marker='o')\n",
    "plt.scatter(actual_values, actual_values, color='red', alpha=0.7, label='Actual Values', marker='x')\n",
    "plt.title('Actual vs Predicted Values')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "325762e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.653841743072161"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_sigmoid = np.mean((y_pred_sigmoid - y_test) ** 2)\n",
    "mse_sigmoid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "38bda267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Actual values from the DataFrame\n",
    "actual_values = y_test # Replace 'actual_values' with the actual column name\n",
    "\n",
    "# Predicted values from the DataFrame\n",
    "predicted_values = y_pred_sigmoid  # Replace 'predicted_values' with the actual column name\n",
    "\n",
    "# Create a scatter plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(actual_values, predicted_values, color='blue', alpha=0.7, label='Predicted Values', marker='o')\n",
    "plt.scatter(actual_values, actual_values, color='red', alpha=0.7, label='Actual Values', marker='x')\n",
    "plt.title('Actual vs Predicted Values')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "831280f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear R2 0.09192294015401081\n",
      "Lasso R2 -0.0032274411449266793\n",
      "SVR Linear R2 0.0962980937584933\n",
      "SVR RBF R2 0.12765529900071382\n",
      "SVR Poly R2 0.05297063322136342\n",
      "SVR Sigmoid R2 -3.7613152720799565\n"
     ]
    }
   ],
   "source": [
    "print(\"Linear R2\", r2_score(y_test, y_pred_linear))\n",
    "print(\"Lasso R2\", r2_score(y_test, y_pred_lasso))\n",
    "print(\"SVR Linear R2\", r2_score(y_test, y_pred_svr_linear))\n",
    "print(\"SVR RBF R2\", r2_score(y_test, y_pred_rbf))\n",
    "print(\"SVR Poly R2\", r2_score(y_test, y_pred_poly))\n",
    "print(\"SVR Sigmoid R2\", r2_score(y_test, y_pred_sigmoid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c064640",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}