dataSet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from googleapiclient.discovery import build\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from IPython.display import JSON\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "source": [
    "# Data Collection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defines an API key and a YouTube channel ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "api_key = 'AIzaSyCYBMm0CU5lg49HR281zWs7i1P2p6pzJc8'\n",
    "channel_id = 'UCX6b17PVsYBQ0ip5gyeme-Q'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "youtube = build(\"youtube\", \"v3\", developerKey=api_key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieving YouTube channel statistics using the YouTube Data API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_channel_stats(youtube, channel_id):\n",
    "        request = youtube.channels().list(\n",
    "        part=\"snippet,contentDetails,statistics\",\n",
    "        id=channel_id)\n",
    "\n",
    "        response = request.execute()\n",
    "\n",
    "        data = dict(Channel_name = response['items'][0]['snippet']['title'],\n",
    "                    Subscribers = response['items'][0]['statistics']['subscriberCount'],\n",
    "                    views = response['items'][0]['statistics']['viewCount'],\n",
    "                    Total_videos = response['items'][0]['statistics']['videoCount'],\n",
    "                    playlist_id = response['items'][0]['contentDetails']['relatedPlaylists']['uploads']\n",
    "        )\n",
    "        return data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_statistics = get_channel_stats(youtube, channel_id)\n",
    "\n",
    "channel_statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "{'Channel_name': 'CrashCourse',\n",
    " 'Subscribers': '15300000',\n",
    " 'views': '1925547276',\n",
    " 'Total_videos': '1514',\n",
    " 'playlist_id': 'UUX6b17PVsYBQ0ip5gyeme-Q'}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A function to retrieve video IDs from a YouTube playlist using the YouTube Data API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_video_ids(youtube, playlist_id):\n",
    "        request = youtube.playlistItems().list(\n",
    "        part=\"contentDetails\",\n",
    "        playlistId=playlist_id,\n",
    "        maxResults = 50)\n",
    "\n",
    "        response = request.execute()\n",
    "\n",
    "\n",
    "        video_ids=[]\n",
    "        for i in range (len(response['items'])):\n",
    "                video_ids.append(response['items'][i]['contentDetails']['videoId'])\n",
    "\n",
    "\n",
    "        next_page_token = response.get('nextPageToken')\n",
    "        more_pages = True\n",
    "\n",
    "        while more_pages:\n",
    "                if next_page_token is None: #we are trying to get all the video ids, BUT bc the max return is 50, we are going through a loop of requests to get all video ids of the channel and storing it inside video_ids array:)\n",
    "                        more_pages = False\n",
    "                else:\n",
    "                        request = youtube.playlistItems().list(\n",
    "                                  part=\"contentDetails\",\n",
    "                                  playlistId=playlist_id,\n",
    "                                  maxResults = 50,\n",
    "                                  pageToken = next_page_token)\n",
    "                        response = request.execute()\n",
    "\n",
    "                        for i in range (len(response['items'])):\n",
    "                                video_ids.append(response['items'][i]['contentDetails']['videoId'])\n",
    "                        next_page_token = response.get('nextPageToken')\n",
    "\n",
    "        return video_ids               \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_ids = get_video_ids(youtube, channel_statistics['playlist_id'])\n",
    "\n",
    "JSON(video_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The length of the 'video_ids' list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(video_ids) #nice, we got all the video ids :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Function to get video details"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function fetches video details from YouTube using its API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_video_details(youtube, video_ids):\n",
    "    all_video_stats = []\n",
    "\n",
    "    for i in range (0, len(video_ids), 50):\n",
    "        request = youtube.videos().list(\n",
    "                # part=\"snippet,statistics\" original,\n",
    "                  part=\"snippet,contentDetails,statistics\", \n",
    "\n",
    "                id=','.join(video_ids[i:i+50]))\n",
    "        response = request.execute()\n",
    "\n",
    "\n",
    "        for video in response['items']:\n",
    "            video_stats = dict(\n",
    "                Title = video['snippet']['title'],\n",
    "                ID = video['id'],\n",
    "                Published_date = video['snippet']['publishedAt'],\n",
    "\n",
    "                #Tags = video['snippet']['tags'], #+roy\n",
    "                Tags = video['snippet'].get('tags', 0),\n",
    "\n",
    "                #Views = video['statistics']['viewCount'],\n",
    "                Views = video['statistics'].get('viewCount', 0),\n",
    "\n",
    "\n",
    "                #Likes = video['statistics']['likeCount'],\n",
    "                Likes = video['statistics'].get('likeCount', 0),\n",
    "\n",
    "                #Comments = video['statistics']['commentCount'],\n",
    "                Comments = video['statistics'].get('commentCount', 0),\n",
    "                \n",
    "                Duration = video['contentDetails']['duration'], #+roy\n",
    "                Captoins = video['contentDetails']['caption'] #+roy\n",
    "                )\n",
    "\n",
    "            all_video_stats.append(video_stats)\n",
    "    return all_video_stats\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_details = get_video_details(youtube, video_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converts the retrieved video details into a pandas DataFrame for further analysis and manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_data = pd.DataFrame(video_details)\n",
    "video_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Saving DataFrame to a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "video_data.to_csv('video_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Start Run From Here and BELOW ONLY !!!!!!! + plus the first cell for libraries. pls ^^"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "source": [
    "# Data Processing and Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reads video data from a CSV file into a DataFrame and displays the first five rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>Title</th>\n",
       "      <th>ID</th>\n",
       "      <th>Published_date</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Views</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Captoins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Why Your Cat Looks Like That: Genetics: Crash ...</td>\n",
       "      <td>YnJPbphsoMY</td>\n",
       "      <td>2024-02-20T17:00:21Z</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>32963</td>\n",
       "      <td>1416</td>\n",
       "      <td>24</td>\n",
       "      <td>PT11M48S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Black American History Arts &amp; Culture | Compil...</td>\n",
       "      <td>bfH3fkIsc5U</td>\n",
       "      <td>2024-02-16T16:30:06Z</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>16596</td>\n",
       "      <td>666</td>\n",
       "      <td>27</td>\n",
       "      <td>PT1H13M13S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Why Are All Humans Unique? Meiosis: Crash Cour...</td>\n",
       "      <td>pj1oFx42d48</td>\n",
       "      <td>2024-02-13T17:00:39Z</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>45926</td>\n",
       "      <td>1394</td>\n",
       "      <td>36</td>\n",
       "      <td>PT12M50S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mitosis and the Cell Cycle: Crash Course Biolo...</td>\n",
       "      <td>skPOXcVvS5c</td>\n",
       "      <td>2024-02-06T17:00:44Z</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>47789</td>\n",
       "      <td>1338</td>\n",
       "      <td>14</td>\n",
       "      <td>PT11M11S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Photosynthesis: The Original Solar Power: Cras...</td>\n",
       "      <td>-ZRsLhaukn8</td>\n",
       "      <td>2024-01-30T17:00:00Z</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>52459</td>\n",
       "      <td>1185</td>\n",
       "      <td>26</td>\n",
       "      <td>PT13M4S</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Title           ID  \\\n",
       "0  Why Your Cat Looks Like That: Genetics: Crash ...  YnJPbphsoMY   \n",
       "1  Black American History Arts & Culture | Compil...  bfH3fkIsc5U   \n",
       "2  Why Are All Humans Unique? Meiosis: Crash Cour...  pj1oFx42d48   \n",
       "3  Mitosis and the Cell Cycle: Crash Course Biolo...  skPOXcVvS5c   \n",
       "4  Photosynthesis: The Original Solar Power: Cras...  -ZRsLhaukn8   \n",
       "\n",
       "         Published_date                                               Tags  \\\n",
       "0  2024-02-20T17:00:21Z  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "1  2024-02-16T16:30:06Z  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "2  2024-02-13T17:00:39Z  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "3  2024-02-06T17:00:44Z  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "4  2024-01-30T17:00:00Z  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "\n",
       "   Views  Likes  Comments    Duration  Captoins  \n",
       "0  32963   1416        24    PT11M48S      True  \n",
       "1  16596    666        27  PT1H13M13S      True  \n",
       "2  45926   1394        36    PT12M50S      True  \n",
       "3  47789   1338        14    PT11M11S      True  \n",
       "4  52459   1185        26     PT13M4S      True  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('video_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the number of rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 1515\n",
      "Number of columns: 9\n"
     ]
    }
   ],
   "source": [
    "num_rows, num_cols = df.shape\n",
    "\n",
    "print(\"Number of rows:\", num_rows)\n",
    "print(\"Number of columns:\", num_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prints the data types of each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Types of columns:\n",
      "Title             object\n",
      "ID                object\n",
      "Published_date    object\n",
      "Tags              object\n",
      "Views              int64\n",
      "Likes              int64\n",
      "Comments           int64\n",
      "Duration          object\n",
      "Captoins            bool\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "column_types = df.dtypes\n",
    "\n",
    "print(\"Types of columns:\")\n",
    "print(column_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are no null values in the DataFrame.\n"
     ]
    }
   ],
   "source": [
    "nulls_exist = df.isnull().any().any()\n",
    "\n",
    "if nulls_exist:\n",
    "    print(\"There are null values in the DataFrame.\")\n",
    "else:\n",
    "    print(\"There are no null values in the DataFrame.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for duplicated values in ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No duplicate values.\n"
     ]
    }
   ],
   "source": [
    "if df['ID'].duplicated().any():\n",
    "    print(\"Duplicate values found.\")\n",
    "else:\n",
    "    print(\"No duplicate values.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Published dates sorted in ascending order and their respective counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.Published_date.sort_values().value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converted boolean captions to binary values (1 for True, 0 for False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>Title</th>\n",
       "      <th>ID</th>\n",
       "      <th>Published_date</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Views</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Captoins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Why Your Cat Looks Like That: Genetics: Crash ...</td>\n",
       "      <td>YnJPbphsoMY</td>\n",
       "      <td>2024-02-20T17:00:21Z</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>32963</td>\n",
       "      <td>1416</td>\n",
       "      <td>24</td>\n",
       "      <td>PT11M48S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Black American History Arts &amp; Culture | Compil...</td>\n",
       "      <td>bfH3fkIsc5U</td>\n",
       "      <td>2024-02-16T16:30:06Z</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>16596</td>\n",
       "      <td>666</td>\n",
       "      <td>27</td>\n",
       "      <td>PT1H13M13S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Why Are All Humans Unique? Meiosis: Crash Cour...</td>\n",
       "      <td>pj1oFx42d48</td>\n",
       "      <td>2024-02-13T17:00:39Z</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>45926</td>\n",
       "      <td>1394</td>\n",
       "      <td>36</td>\n",
       "      <td>PT12M50S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mitosis and the Cell Cycle: Crash Course Biolo...</td>\n",
       "      <td>skPOXcVvS5c</td>\n",
       "      <td>2024-02-06T17:00:44Z</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>47789</td>\n",
       "      <td>1338</td>\n",
       "      <td>14</td>\n",
       "      <td>PT11M11S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Photosynthesis: The Original Solar Power: Cras...</td>\n",
       "      <td>-ZRsLhaukn8</td>\n",
       "      <td>2024-01-30T17:00:00Z</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>52459</td>\n",
       "      <td>1185</td>\n",
       "      <td>26</td>\n",
       "      <td>PT13M4S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Title           ID  \\\n",
       "0  Why Your Cat Looks Like That: Genetics: Crash ...  YnJPbphsoMY   \n",
       "1  Black American History Arts & Culture | Compil...  bfH3fkIsc5U   \n",
       "2  Why Are All Humans Unique? Meiosis: Crash Cour...  pj1oFx42d48   \n",
       "3  Mitosis and the Cell Cycle: Crash Course Biolo...  skPOXcVvS5c   \n",
       "4  Photosynthesis: The Original Solar Power: Cras...  -ZRsLhaukn8   \n",
       "\n",
       "         Published_date                                               Tags  \\\n",
       "0  2024-02-20T17:00:21Z  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "1  2024-02-16T16:30:06Z  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "2  2024-02-13T17:00:39Z  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "3  2024-02-06T17:00:44Z  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "4  2024-01-30T17:00:00Z  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "\n",
       "   Views  Likes  Comments    Duration  Captoins  \n",
       "0  32963   1416        24    PT11M48S         1  \n",
       "1  16596    666        27  PT1H13M13S         1  \n",
       "2  45926   1394        36    PT12M50S         1  \n",
       "3  47789   1338        14    PT11M11S         1  \n",
       "4  52459   1185        26     PT13M4S         1  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Captoins'] = df['Captoins'].map({True: 1, False: 0})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalizing Views, Comments and Likes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>ID</th>\n",
       "      <th>Published_date</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Views</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Why Your Cat Looks Like That: Genetics: Crash ...</td>\n",
       "      <td>YnJPbphsoMY</td>\n",
       "      <td>2024-02-20T17:00:21Z</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>0.002072</td>\n",
       "      <td>0.005684</td>\n",
       "      <td>0.000810</td>\n",
       "      <td>PT11M48S</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Black American History Arts &amp; Culture | Compil...</td>\n",
       "      <td>bfH3fkIsc5U</td>\n",
       "      <td>2024-02-16T16:30:06Z</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>0.001043</td>\n",
       "      <td>0.002673</td>\n",
       "      <td>0.000911</td>\n",
       "      <td>PT1H13M13S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Why Are All Humans Unique? Meiosis: Crash Cour...</td>\n",
       "      <td>pj1oFx42d48</td>\n",
       "      <td>2024-02-13T17:00:39Z</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>0.005596</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>PT12M50S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mitosis and the Cell Cycle: Crash Course Biolo...</td>\n",
       "      <td>skPOXcVvS5c</td>\n",
       "      <td>2024-02-06T17:00:44Z</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>0.003004</td>\n",
       "      <td>0.005371</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>PT11M11S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Photosynthesis: The Original Solar Power: Cras...</td>\n",
       "      <td>-ZRsLhaukn8</td>\n",
       "      <td>2024-01-30T17:00:00Z</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>0.003297</td>\n",
       "      <td>0.004757</td>\n",
       "      <td>0.000878</td>\n",
       "      <td>PT13M4S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Title           ID  \\\n",
       "0  Why Your Cat Looks Like That: Genetics: Crash ...  YnJPbphsoMY   \n",
       "1  Black American History Arts & Culture | Compil...  bfH3fkIsc5U   \n",
       "2  Why Are All Humans Unique? Meiosis: Crash Cour...  pj1oFx42d48   \n",
       "3  Mitosis and the Cell Cycle: Crash Course Biolo...  skPOXcVvS5c   \n",
       "4  Photosynthesis: The Original Solar Power: Cras...  -ZRsLhaukn8   \n",
       "\n",
       "         Published_date                                               Tags  \\\n",
       "0  2024-02-20T17:00:21Z  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "1  2024-02-16T16:30:06Z  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "2  2024-02-13T17:00:39Z  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "3  2024-02-06T17:00:44Z  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "4  2024-01-30T17:00:00Z  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "\n",
       "      Views     Likes  Comments    Duration  Captoins  \n",
       "0  0.002072  0.005684  0.000810    PT11M48S         1  \n",
       "1  0.001043  0.002673  0.000911  PT1H13M13S         1  \n",
       "2  0.002887  0.005596  0.001215    PT12M50S         1  \n",
       "3  0.003004  0.005371  0.000473    PT11M11S         1  \n",
       "4  0.003297  0.004757  0.000878     PT13M4S         1  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to normalize a column using Min-Max normalization:\n",
    "def min_max_normalize(column):\n",
    "    min_val = min(column)\n",
    "    max_val = max(column)\n",
    "    normalized_column = (column - min_val) / (max_val - min_val)\n",
    "    return normalized_column\n",
    "\n",
    "# Normalize each column\n",
    "df['Views'] = min_max_normalize(df['Views'])\n",
    "df['Likes'] = min_max_normalize(df['Likes'])\n",
    "df['Comments'] = min_max_normalize(df['Comments'])\n",
    "\n",
    "\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Formatted the 'Published_date' column to datetime objects and then converted it to a specific string format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Title</th>\n",
       "      <th>ID</th>\n",
       "      <th>Published_date</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Views</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Duration</th>\n",
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       "      <td>Why Your Cat Looks Like That: Genetics: Crash ...</td>\n",
       "      <td>YnJPbphsoMY</td>\n",
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       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>Why Are All Humans Unique? Meiosis: Crash Cour...</td>\n",
       "      <td>pj1oFx42d48</td>\n",
       "      <td>2024-02-13 17:00:39</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>0.005596</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>PT12M50S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mitosis and the Cell Cycle: Crash Course Biolo...</td>\n",
       "      <td>skPOXcVvS5c</td>\n",
       "      <td>2024-02-06 17:00:44</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>0.003004</td>\n",
       "      <td>0.005371</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>PT11M11S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Photosynthesis: The Original Solar Power: Cras...</td>\n",
       "      <td>-ZRsLhaukn8</td>\n",
       "      <td>2024-01-30 17:00:00</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>0.003297</td>\n",
       "      <td>0.004757</td>\n",
       "      <td>0.000878</td>\n",
       "      <td>PT13M4S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Title           ID  \\\n",
       "0  Why Your Cat Looks Like That: Genetics: Crash ...  YnJPbphsoMY   \n",
       "1  Black American History Arts & Culture | Compil...  bfH3fkIsc5U   \n",
       "2  Why Are All Humans Unique? Meiosis: Crash Cour...  pj1oFx42d48   \n",
       "3  Mitosis and the Cell Cycle: Crash Course Biolo...  skPOXcVvS5c   \n",
       "4  Photosynthesis: The Original Solar Power: Cras...  -ZRsLhaukn8   \n",
       "\n",
       "        Published_date                                               Tags  \\\n",
       "0  2024-02-20 17:00:21  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "1  2024-02-16 16:30:06  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "2  2024-02-13 17:00:39  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "3  2024-02-06 17:00:44  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "4  2024-01-30 17:00:00  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "\n",
       "      Views     Likes  Comments    Duration  Captoins  \n",
       "0  0.002072  0.005684  0.000810    PT11M48S         1  \n",
       "1  0.001043  0.002673  0.000911  PT1H13M13S         1  \n",
       "2  0.002887  0.005596  0.001215    PT12M50S         1  \n",
       "3  0.003004  0.005371  0.000473    PT11M11S         1  \n",
       "4  0.003297  0.004757  0.000878     PT13M4S         1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dateutil import parser\n",
    "\n",
    "df['Published_date'] = pd.to_datetime(df['Published_date'])\n",
    "df['Published_date'] = df['Published_date'].dt.strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transforming duration into seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Title</th>\n",
       "      <th>ID</th>\n",
       "      <th>Published_date</th>\n",
       "      <th>Tags</th>\n",
       "      <th>Views</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Captoins</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Why Your Cat Looks Like That: Genetics: Crash ...</td>\n",
       "      <td>YnJPbphsoMY</td>\n",
       "      <td>2024-02-20 17:00:21</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>0.002072</td>\n",
       "      <td>0.005684</td>\n",
       "      <td>0.000810</td>\n",
       "      <td>708.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>Black American History Arts &amp; Culture | Compil...</td>\n",
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       "      <td>2024-02-16 16:30:06</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>0.001043</td>\n",
       "      <td>0.002673</td>\n",
       "      <td>0.000911</td>\n",
       "      <td>4393.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Why Are All Humans Unique? Meiosis: Crash Cour...</td>\n",
       "      <td>pj1oFx42d48</td>\n",
       "      <td>2024-02-13 17:00:39</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>0.005596</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>770.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mitosis and the Cell Cycle: Crash Course Biolo...</td>\n",
       "      <td>skPOXcVvS5c</td>\n",
       "      <td>2024-02-06 17:00:44</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>0.003004</td>\n",
       "      <td>0.005371</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>671.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Photosynthesis: The Original Solar Power: Cras...</td>\n",
       "      <td>-ZRsLhaukn8</td>\n",
       "      <td>2024-01-30 17:00:00</td>\n",
       "      <td>['vlogbrothers', 'Crash Course', 'crashcourse'...</td>\n",
       "      <td>0.003297</td>\n",
       "      <td>0.004757</td>\n",
       "      <td>0.000878</td>\n",
       "      <td>784.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Title           ID  \\\n",
       "0  Why Your Cat Looks Like That: Genetics: Crash ...  YnJPbphsoMY   \n",
       "1  Black American History Arts & Culture | Compil...  bfH3fkIsc5U   \n",
       "2  Why Are All Humans Unique? Meiosis: Crash Cour...  pj1oFx42d48   \n",
       "3  Mitosis and the Cell Cycle: Crash Course Biolo...  skPOXcVvS5c   \n",
       "4  Photosynthesis: The Original Solar Power: Cras...  -ZRsLhaukn8   \n",
       "\n",
       "        Published_date                                               Tags  \\\n",
       "0  2024-02-20 17:00:21  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "1  2024-02-16 16:30:06  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "2  2024-02-13 17:00:39  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "3  2024-02-06 17:00:44  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "4  2024-01-30 17:00:00  ['vlogbrothers', 'Crash Course', 'crashcourse'...   \n",
       "\n",
       "      Views     Likes  Comments  Duration  Captoins  \n",
       "0  0.002072  0.005684  0.000810     708.0         1  \n",
       "1  0.001043  0.002673  0.000911    4393.0         1  \n",
       "2  0.002887  0.005596  0.001215     770.0         1  \n",
       "3  0.003004  0.005371  0.000473     671.0         1  \n",
       "4  0.003297  0.004757  0.000878     784.0         1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Duration'] = pd.to_timedelta(df['Duration']).dt.total_seconds()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descriptive statistics for numeric columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Views</th>\n",
       "      <th>Likes</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Captoins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1515.000000</td>\n",
       "      <td>1515.000000</td>\n",
       "      <td>1515.000000</td>\n",
       "      <td>1515.000000</td>\n",
       "      <td>1515.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.080092</td>\n",
       "      <td>0.071519</td>\n",
       "      <td>0.034703</td>\n",
       "      <td>667.205281</td>\n",
       "      <td>0.977558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.113859</td>\n",
       "      <td>0.090076</td>\n",
       "      <td>0.062286</td>\n",
       "      <td>237.111505</td>\n",
       "      <td>0.148166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.011012</td>\n",
       "      <td>0.016425</td>\n",
       "      <td>0.003983</td>\n",
       "      <td>597.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.033993</td>\n",
       "      <td>0.040165</td>\n",
       "      <td>0.014583</td>\n",
       "      <td>678.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.100725</td>\n",
       "      <td>0.091372</td>\n",
       "      <td>0.036896</td>\n",
       "      <td>753.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4393.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Views        Likes     Comments     Duration     Captoins\n",
       "count  1515.000000  1515.000000  1515.000000  1515.000000  1515.000000\n",
       "mean      0.080092     0.071519     0.034703   667.205281     0.977558\n",
       "std       0.113859     0.090076     0.062286   237.111505     0.148166\n",
       "min       0.000000     0.000000     0.000000    39.000000     0.000000\n",
       "25%       0.011012     0.016425     0.003983   597.000000     1.000000\n",
       "50%       0.033993     0.040165     0.014583   678.000000     1.000000\n",
       "75%       0.100725     0.091372     0.036896   753.500000     1.000000\n",
       "max       1.000000     1.000000     1.000000  4393.000000     1.000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Views', 'Likes', 'Comments','Duration','Captoins']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descriptive statistics for non-numeric columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Title</th>\n",
       "      <th>ID</th>\n",
       "      <th>Published_date</th>\n",
       "      <th>Tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1515</td>\n",
       "      <td>1347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Why Your Cat Looks Like That: Genetics: Crash ...</td>\n",
       "      <td>YnJPbphsoMY</td>\n",
       "      <td>2024-02-20 17:00:21</td>\n",
       "      <td>['John Green', 'Hank Green', 'vlogbrothers', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Title           ID  \\\n",
       "count                                                1515         1515   \n",
       "unique                                               1515         1515   \n",
       "top     Why Your Cat Looks Like That: Genetics: Crash ...  YnJPbphsoMY   \n",
       "freq                                                    1            1   \n",
       "\n",
       "             Published_date                                               Tags  \n",
       "count                  1515                                               1515  \n",
       "unique                 1515                                               1347  \n",
       "top     2024-02-20 17:00:21  ['John Green', 'Hank Green', 'vlogbrothers', '...  \n",
       "freq                      1                                                117  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculates the average duration of videos, then displays a boxplot showing the distribution of video durations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average duration of the videos is 667.2052805280528 seconds\n",
      "The average duration of the videos is 11.120088008800879 minutes\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "average_duration = df['Duration'].mean()\n",
    "print(\"The average duration of the videos is\", average_duration, \"seconds\")\n",
    "print(\"The average duration of the videos is\", average_duration / 60, \"minutes\")\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "df.boxplot(['Duration'], color='orange')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Displays the counts and distribution of captions in the dataset, along with the percentage of each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counts of Captions:\n",
      "Captoins\n",
      "1    1481\n",
      "0      34\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking for unique values in Captions\n",
    "value_counts = df['Captoins'].value_counts()\n",
    "\n",
    "print(\"Counts of Captions:\")\n",
    "print(value_counts)\n",
    "\n",
    "ax = sns.countplot(x='Captoins', data=df, color='pink')\n",
    "\n",
    "total_count = len(df['Captoins'])\n",
    "for p in ax.patches:\n",
    "    ax.text(p.get_x() + p.get_width() / 2., p.get_height() + 10, f'{p.get_height() / total_count:.2%}', ha=\"center\")\n",
    "\n",
    "plt.title('Distribution of Captions')\n",
    "plt.xlabel('Captions')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizes the correlation between numeric columns using a heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "selected_columns = ['Views', 'Likes', 'Comments', 'Duration', 'Captoins'] \n",
    "numeric_df = df[selected_columns]\n",
    "\n",
    "# Create correlation matrix\n",
    "correlation_matrix = numeric_df.corr()\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\")\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizes the distribution of Views, Likes, and Comments using boxplots, each in a separate subplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "# Create subplots for each variable\n",
    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 5))\n",
    "\n",
    "# Views boxplot\n",
    "sns.boxplot(x='Views', data=df, ax=axes[0], color=\"#FF5733\")\n",
    "axes[0].set_title('Boxplot of Views')\n",
    "\n",
    "# Likes boxplot \n",
    "sns.boxplot(x='Likes', data=df, ax=axes[1], color=\"#C70039\")\n",
    "axes[1].set_title('Boxplot of Likes')\n",
    "\n",
    "# Comments boxplot\n",
    "sns.boxplot(x='Comments', data=df, ax=axes[2], color= \"#E5317F\")\n",
    "axes[2].set_title('Boxplot of Comments')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a bar plot to display the top 10 most viewed videos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top10_videos = df.sort_values(by='Views', ascending=False).head(10)\n",
    "top10_videos.head(10)\n",
    "ax1 = sns.barplot(x='Views', y='Title', data=top10_videos,orient='h')\n",
    "\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a bar plot showing the total views per year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IAAAgiThqdCqrqko9Qq1sbPMCANB4NS31ANSt8iZNMvIXz2TeomWlHmW9OnVsk9En9y/1GAAAkEQcNUrzFi3La2+9V+oxAABgo+K0OgAAgIgjAACAJOIIAAAgiTgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEgijgAAAJKIIwAAgCTiCAAAIIk4AgAASCKOAAAAkogjAACAJOIIAAAgiTgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEgijgAAAJKIIwAAgCTiCAAAIIk4AgAASCKOAAAAkogjAACAJOIIAAAgiTgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEgijgAAAJKIIwAAgCTiCAAAIIk4AgAASCKOAAAAkogjAACAJOIIAAAgiTgCAABIUuI4WrhwYYYPH57evXunf//+ueqqq7J69eokyejRo1NRUVHjbdy4caUcFwAAaMSaluoLFwqFDB8+PFtssUUeeOCBLFu2LJdcckmaNGmSiy66KHPmzMn555+fb37zm9Wf07p161KNCwAANHIle+Ro7ty5mTZtWq666qrsuuuu2WeffTJ8+PA8+uijSZI5c+aka9eu2WqrrarfWrRoUapxAQCARq5kjxxttdVWueuuu9KhQ4cax5cvX57ly5dn4cKF2Wmnnb7016msrPzS17ExKS8vL/UItbap3UYAANSvDb2/WbI42mKLLdK/f//q96uqqjJu3Ljst99+mTNnTsrKynLbbbfl6aefTtu2bXPaaafVOMVuQ82YMaMux27QWrRoka5du5Z6jFqbOXNmVq5cWeoxAADYxJUsjj5tzJgxeeWVV/Lwww/nb3/7W8rKytK5c+eccsopmTx5ci677LK0bt06AwcOrNX1du/efaN8NGVTUlFRUeoRAABoxCorKzfoQZMGEUdjxozJfffdl7Fjx6ZLly7Zddddc/DBB6dt27ZJkt122y2vv/56HnzwwVrHUXl5uThq4Nw+AAA0BCX/PUejRo3KvffemzFjxuQb3/hGkqSsrKw6jD7WuXPnLFy4sAQTAgAAm4KSxtFNN92Uhx56KNddd12OOOKI6uPXX399hgwZUuOyr732Wjp37lzPEwIAAJuKksXRnDlzcsstt+TMM89Mr169snjx4uq3gw8+OJMnT87dd9+dN954I7/4xS8yYcKEnH766aUaFwAAaORK9pyjJ598MpWVlbn11ltz66231vjYzJkzc/311+eGG27I9ddfn+233z7XXnttevbsWaJpAQCAxq5kcXTWWWflrLPO+tyPH3rooTn00EPrcSIAAGBTVvIXZAAav8qqqlKPUCsb27wAQN1oEC/lDTRu5U2aZOQvnsm8RctKPcp6derYJqNP7r/+CwIAjY44AurFvEXL8tpb75V6DACAz+W0OgAAgIgjAACAJOIIAAAgiTgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEgijgAAAJKIIwAAgCTiCAAAIIk4AgAASCKOAAAAkogjAACAJOIIAAAgiTgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEgijgAAAJKIIwAAgCTiCAAAIIk4AgAASCKOAAAAkogjAACAJOIIAAAgiTgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEgijgAAAJKIIwAAgCTiCAAAIIk4AgAASCKOAAAAkogjAACAJOIIAAAgiTgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEgijgAAAJKIIwAAgCTiCAAAIIk4AgAASCKOAAAAkogjAACAJOIIAAAgiTgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEgijgAAAJKIIwAAgCTiCAAAIIk4AgAASCKOAAAAkogjAACAJOIIAAAgiTgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEgijgAAAJKIIwAAgCTiCAAAIIk4AgAASCKOAAAAkogjAACAJOIIAAAgiTgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEgijgAAAJKIIwAAgCTiCAAAIIk4AgAASCKOAAAAkogjAACAJOIIAAAgiTgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEhS4jhauHBhhg8fnt69e6d///656qqrsnr16iTJ/PnzM2TIkOy1114ZNGhQnn322VKOCgAANHIli6NCoZDhw4dn5cqVeeCBBzJ27Nj88Y9/zM9+9rMUCoUMHTo0HTp0yPjx43P00Ufn7LPPzoIFC0o1LgAA0Mg1LdUXnjt3bqZNm5bnnnsuHTp0SJIMHz48V199dQ488MDMnz8/Dz30UFq2bJmdd945zz//fMaPH59hw4aVamQAAKARK9kjR1tttVXuuuuu6jD62PLlyzN9+vR07do1LVu2rD7eq1evTJs2rZ6nBAAANhUle+Roiy22SP/+/avfr6qqyrhx47Lffvtl8eLF6dixY43Lt2/fPu+8806tv05lZeWXnnVjUl5eXuoRam1Tu402Rb4vAYBS2tB/10sWR582ZsyYvPLKK3n44Yfzn//5n2nWrFmNjzdr1ixr1qyp9fXOmDGjrkZs8Fq0aJGuXbuWeoxamzlzZlauXFnqMSgS35cAwMaiQcTRmDFjct9992Xs2LHp0qVLmjdvnqVLl9a4zJo1a7LZZpvV+rq7d+++Uf7UelNSUVFR6hHgM3xfAkDjUVlZuUEPmpQ8jkaNGpUHH3wwY8aMyTe+8Y0kydZbb53Zs2fXuNy77777mVPtNkR5ebk4auDcPjREvi8BYNNT0t9zdNNNN+Whhx7KddddlyOOOKL6eI8ePfK3v/0tq1atqj42derU9OjRoxRjAgAAm4CSxdGcOXNyyy235Mwzz0yvXr2yePHi6rfevXtn2223zYgRIzJr1qzccccdeemll3L88ceXalwAAKCRK9lpdU8++WQqKytz66235tZbb63xsZkzZ+aWW27JpZdemmOPPTY77rhjbr755my33XYlmhYAAGjsShZHZ511Vs4666zP/fiOO+6YcePG1eNEAADApqykzzkCAABoKMQRAABAxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxxEaksqqq1CPUysY2LwDApq5pqQeADVXepElG/uKZzFu0rNSjrFenjm0y+uT+pR4DAIBaEEdsVOYtWpbX3nqv1GMAANAIOa0OAAAg4ggAACCJOAIAAEgijgAAAJKIIwAAgCTiCAAAIIk4AgAASCKOAAAAkogjAACAJOIIAAAgiTgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEgijgAAAJKIIwAAgCTiCAAAIIk4AgAASCKOAAAAkogjAACAJOIIAAAgiTgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEgijqBBqKyqKvUItbYxzgwA8EWalnoAIClv0iQjf/FM5i1aVupRNkinjm0y+uT+pR4DAKBOiSNoIOYtWpbX3nqv1GMAAGyynFYHAAAQcQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAASRpIHK1ZsyZHHnlkJk2aVH1s9OjRqaioqPE2bty4Ek4JAAA0Zk1LPcDq1atz/vnnZ9asWTWOz5kzJ+eff36++c1vVh9r3bp1fY8HAABsIkr6yNHs2bNzwgkn5I033vjMx+bMmZOuXbtmq622qn5r0aJFCaYEAAA2BSWNoxdffDF9+vTJL3/5yxrHly9fnoULF2annXYqzWAAAMAmp6Sn1Z188snrPD5nzpyUlZXltttuy9NPP522bdvmtNNOq3GK3YaqrKz8smNuVMrLy0s9Qq1t6G1kt4anMe+3qf3dAQCN2Yb+u/6l42j16tWZOXNmOnXqlM033/zLXl2SZO7cuSkrK0vnzp1zyimnZPLkybnsssvSunXrDBw4sFbXNWPGjDqZaWPQokWLdO3atdRj1NrMmTOzcuXKL7yM3RqmxrzfhuwGADQutY6j2bNn55JLLsnFF1+cXXbZJSeeeGLmzZuXFi1a5NZbb81+++33pYc65phjcvDBB6dt27ZJkt122y2vv/56HnzwwVrHUffu3TfKn1pvSioqKko9QtE05t2Sxr1fY94NADY1lZWVG/SgSa3j6Morr8wOO+yQTp065eGHH87777+fZ599NuPHj8/VV1+d3/zmN//UwJ9UVlZWHUYf69y5c1544YVaX1d5ebk4auAa8+3TmHdLGvd+jXk3AGDdav2CDC+99FLOPffcbLnllpk4cWIGDhyYDh065Mgjj8zcuXPrZKjrr78+Q4YMqXHstddeS+fOnevk+gEAAD6t1nG0+eab5913383bb7+dadOm5etf/3qS5NVXX0379u3rZKiDDz44kydPzt1335033ngjv/jFLzJhwoScfvrpdXL9AAAAn1br0+qOPfbY/K//9b/SrFmzfPWrX02/fv3y4IMP5qc//WnOOeecOhlqzz33zPXXX58bbrgh119/fbbffvtce+216dmzZ51cPwAAwKfVOo7OO++87LnnnnnzzTdz5JFHpry8PNttt12uu+66HHzwwf/0IDNnzqzx/qGHHppDDz30n74+AACA2qh1HF144YXp379/jj766Gy55ZZJkoMOOqjOBwMAAKhPtX7O0bbbbpv77rsv/fv3z/HHH5/rr78+f/nLX1JVVVWM+QAAAOpFrR85+v73v5/vf//7WbZsWSZNmpTnn38+F198cZYuXZr9998/P/vZz4owJgAAQHHV+pGjj1VWVqasrCzNmzdPmzZtsnz58rz22mt1ORsAAEC9qfUjRyNGjMhf/vKXvPXWW6moqMjee++dM844I/vss0+dvZQ3AABAfat1HE2dOjXz58/P/vvvn/79+2fvvfdOt27d/DZ5AABgo1brOHriiSeyaNGiTJkyJZMnT8748eOzYMGCdO/ePfvss0/OPvvsYswJAABQVLWOoyTp2LFjBg0alAMPPDBTpkzJxIkT88gjj+Svf/2rOAIAADZKtY6jp556KpMmTcqLL76Y1157LTvuuGMOOOCA3HTTTenTp08xZgQAACi6f+qXwPbt2zcnnnhi+vfvn2222aYYcwEAANSrWsfRCy+8kLKysixfvjxvvPFG2rVrlzVr1qR169bFmA8AAKBe1Pr3HH344YcZOXJkevfuneOPPz4LFy7MxRdfnO9+97tZtmxZMWYEAAAoulrH0U9/+tPMnj07v/nNb9K8efMkybBhw7JkyZKMHj26zgcEAACoD7WOoyeeeCKXXnppKioqqo9VVFRk1KhRefrpp+t0OAAAgPpS6zj64IMP0qJFi88cr6qqSmVlZZ0MBQAAUN9qHUcDBgzI2LFjs3z58upj8+fPz+jRo3PQQQfV6XAAAAD1pdZxdPnll6dJkybp3bt3Vq5cmeOOOy6HHXZYtthii1x22WXFmBEAAKDoav1S3ptvvnluvPHGzJ8/P3PmzMnatWvTqVOn7LzzzsWYDwAAoF5sUBwtWLAg2267bcrKyrJgwYIkSXl5ebp06VLjMkmy3XbbFWFMAACA4tqgODrkkEPy3HPPpV27dhkwYEDKyspSKBRSVlZWfZmP33/11VeLNiwAAECxbFAcbb755rnmmmty9NFH5w9/+EOaNKn1U5UAAAAatA2Ko8suuyy/+93vcuaZZ2bLLbfMoEGDctRRR2X33Xcv9nwAAAD1YoPiaPDgwRk8eHCWL1+eiRMn5ve//31OOOGE7LDDDjniiCMyePDgfO1rXyv2rAAAAEVTq1era926dY455pgcc8wxWb58ef7whz/k97//fW6//fZUVFRk8ODBOfXUU4s1KwAAQNH8008eat26db75zW/m9ttvzx133JG1a9fmqquuqsvZAAAA6k2tf89R8tEr002ePDlPPPFEJk6cmBUrVuTQQw/NBRdcUNfzAQAA1IsNjqO1a9fmz3/+c/7whz/kySefzIoVK3LQQQdl5MiROfDAA9OsWbNizgkAAFBUGxRHF1xwQZ566qmsWLEiffv2zYUXXpiBAwemVatWxZ4PAACgXmxQHC1YsCDnnntuDj/88LRr167YMwEAANS7DYqjBx54oNhzAAAAlNQ//Wp1AAAAjYk4AgAAiDgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEgijgAAAJKIIwAAgCTiCAAAIIk4AgAASCKOAAAAkogjAACAJOIIAAAgiTgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEgijgAAAJKIIwAAgCTiCAAAIIk4AgAASCKOAAAAkogjAACAJOIIAAAgiTgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEgijgC+lMqqqlKPUCsb27wAUJ+alnoAgI1ZeZMmGfmLZzJv0bJSj7JenTq2yeiT+5d6DABosMQRwJc0b9GyvPbWe6UeAwD4kpxWBwAAEHEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEkaSBytWbMmRx55ZCZNmlR9bP78+RkyZEj22muvDBo0KM8++2wJJwQAABq7ksfR6tWrc95552XWrFnVxwqFQoYOHZoOHTpk/PjxOfroo3P22WdnwYIFJZwUAABozJqW8ovPnj07559/fgqFQo3jL7zwQubPn5+HHnooLVu2zM4775znn38+48ePz7Bhw0o0LQAA0JiV9JGjF198MX369Mkvf/nLGsenT5+erl27pmXLltXHevXqlWnTptXzhAAAwKaipI8cnXzyyes8vnjx4nTs2LHGsfbt2+edd96p9deorKz8p2bbWJWXl5d6hFrb0NvIbg1PY97PbknKylLepORnX9dKZVVV8qmzEQBgQ//tK2kcfZ6VK1emWbNmNY41a9Ysa9asqfV1zZgxo67GavBatGiRrl27lnqMWps5c2ZWrlz5hZexW8PUmPez20e7jfzFM5m3aFk9TfbldOrYJqNP7p9XXnllvfsBwLo0yDhq3rx5li5dWuPYmjVrstlmm9X6urp3775R/mR3U1JRUVHqEYqmMe+WNO797PaReYuW5bW33iviNHWvMd92APxzKisrN+hBkwYZR1tvvXVmz55d49i77777mVPtNkR5ebk4auAa8+3TmHdLGvd+dtt4Nfb9ACieBnkyeY8ePfK3v/0tq1atqj42derU9OjRo4RTAQAAjVmDjKPevXtn2223zYgRIzJr1qzccccdeemll3L88ceXejQAAKCRapBxVF5enltuuSWLFy/Osccem9/+9re5+eabs91225V6NAAAoJFqMM85mjlzZo33d9xxx4wbN65E0wAAAJuaBvnIEQAAQH0TRwAAABFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQJIGHkd/+MMfUlFRUeNt+PDhpR4LAABohJqWeoAvMnv27Bx88MEZNWpU9bHmzZuXcCIAAKCxatBxNGfOnHTp0iVbbbVVqUcBAAAauQZ9Wt2cOXOy0047lXoMAABgE9BgHzkqFAqZN29enn322dx+++2prKzM4YcfnuHDh6dZs2YbfD2VlZVFnLLhKS8vL/UItbaht5HdGp7GvJ/dNs7dkk3v730A1m9D/21osHG0YMGCrFy5Ms2aNcvPfvazvPnmmxk9enRWrVqVkSNHbvD1zJgxo4hTNiwtWrRI165dSz1Grc2cOTMrV678wsvYrWFqzPvZbePcLdmw/QBgXRpsHG2//faZNGlS2rRpk7Kysuy+++6pqqrKBRdckBEjRmzwTzS7d+++0f70c1NRUVFR6hGKpjHvljTu/ey28Wrs+wFQe5WVlRv0oEmDjaMkadu2bY33d95556xevTrLli1Lu3btNug6ysvLxVED15hvn8a8W9K497Pbxqux7wdA8TTYF2R45pln0qdPnxqnRrz66qtp27btBocRAADAhmqwcdSzZ880b948I0eOzNy5c/PUU0/lpz/9ac4444xSjwYAADRCDfa0utatW+fuu+/Oj3/84xx33HFp1apVTjrpJHEEAAAURYONoyTZddddc++995Z6DAAAYBPQYE+rAwAAqE/iCAAAIOIIAAAgiTgCAABIIo4A2ERVVlWVeoRa2djmBdgYNehXqwOAYilv0iQjf/FM5i1aVupR1qtTxzYZfXL/Uo8B0OiJIwA2WfMWLctrb71X6jEAaCCcVgcAABBxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwDQ6FRWVZV6hFrZ2OYFGq+mpR4AAKhb5U2aZOQvnsm8RctKPcp6derYJqNP7l/qMQCSiCMAaJTmLVqW1956r9RjAGxUnFYHAAAQcQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQBJxBAAAkEQcAQAAJBFHAAAAScQRAABAEnEEAGxkKquqSj1CrdRm3sa8G2wMmpZ6AACA2ihv0iQjf/FM5i1aVupR1qtTxzYZfXL/Db58Y94NNgbiCADY6MxbtCyvvfVeqccoisa8GzR0TqsDAACIOAIAAEgijgAAAJKIIwAAgCTiCAAAIIk4AgAASCKOAAAAkogjAACAJOIIAAAgiTgCAABIIo4AAACSiCMAAIAk4ggAACCJOAIAAEgijgAAAJKIIwAAgCTiCAAAIIk4AgAASCKOAAAAkogjAADqQWVVValHqLWNcWa+nKalHgAAgMavvEmTjPzFM5m3aFmpR9kgnTq2yeiT+5d6DOqZOAIAoF7MW7Qsr731XqnHgM/ltDoAAICIIwAAgCTiCAAAIIk4AgAASCKOAADgS9vYXvZ7Y5u3vjToV6tbvXp1rrzyyjzxxBPZbLPNcvrpp+f0008v9VgAAFDDxvRS5V6m/PM16Dj66U9/mpdffjn33XdfFixYkIsuuijbbbddDj/88FKPBgAANXip8o1fg42jFStW5Ne//nXuvPPOdOvWLd26dcusWbPywAMPiCMAAKDONdjnHL322mtZu3ZtevbsWX2sV69emT59eqqcIwkAANSxBvvI0eLFi7PlllumWbNm1cc6dOiQ1atXZ+nSpWnXrt0Xfn6hUEiSrFmzJuXl5f/vA2VlKW/SYJtwnSqrqpL/f5/1KS8vz67btEmz8rIiT/Xl7bjVFqmsrExlZeUGXd5uDUdj3s9u/8/GtFvSuPezW02NeT+7NRyNeb9a/3e3kd1/Xtd95493LaznPnVZYX2XKJEJEybk+uuvzx//+MfqY/Pnz8+hhx6ap556Kttss80Xfv6aNWsyY8aMYo8JAABsJLp3717jwZdPa7CPHDVv3jxr1qypcezj9zfbbLP1fn7Tpk3TvXv3NGnSJGVlDb/gAQCA4igUCqmqqkrTpl+cPw02jrbeeussWbIka9eurV5i8eLF2WyzzbLFFlus9/ObNGnyhVUIAADwSQ325MHdd989TZs2zbRp06qPTZ06tfrRIAAAgLrUYCujRYsWOeaYY3LFFVfkpZdeysSJE3PPPffk1FNPLfVoAABAI9RgX5AhSVauXJkrrrgiTzzxRFq3bp3vfve7GTJkSKnHAgAAGqEGHUcAAAD1pcGeVgcAAFCfxBEAAEDEEQAAQBJxtE4LFy7M8OHD07t37/Tv3z9XXXVVVq9enSSZP39+hgwZkr322iuDBg3Ks88+u87r+O1vf5vvfOc76/zYyJEjc+ONNxZt/i9SrN3WrFmTq6++OgceeGD23XffDB06NO+8807R9/mkYu22YsWKjBw5Mn369Mm+++6byy67LB988EHR9/m0Yn9fJsldd92VAQMGFGX+L1Ks3ZYtW5aKiooab3369Cn6Pp9UzNvtgQceyNe//vXsvffeGT58eJYuXVrMVdapGPu9+eabn7ndPn6bPHlyveyVFO+2W716dUaNGpW+ffumb9++ufzyy7NixYqi7/NJxdrtww8/zJgxY9KvX7/st99+ufrqq7N27dqi7/NpX2a/8ePH5/DDD0/Pnj3zrW99K1OnTq3x8f/8z/9M//7907Nnz1xyySVZuXJlve2VFHe3j5XqfkqxdmsI91GS4u3XEO6n1Mf3Zb3dRylQQ1VVVeGEE04onHHGGYX/+3//b2Hy5MmFgQMHFn7yk58UqqqqCoMHDy6cf/75hdmzZxduu+22Qo8ePQpvvfVWjet4/vnnCz169Ciccsopn7n+O+64o9ClS5fCDTfcUF8rVSvmbmPGjCkceuihhUmTJhVmzZpVOOusswrHHXdcoaqqaqPf7dJLLy0cffTRhRkzZhRefvnlwlFHHVUYOXJkvez1sWJ/XxYKhcIbb7xR6NGjR+Hggw+uj5WqFXO3KVOmFHr37l1YtGhR9du7777bKHZ77LHHCnvuuWfh97//fWHmzJmF448/vvD973+/3nYrFIq339q1a2vcZosWLSqcd955heOOO66wZs2ajXq3QqFQuOaaawpHHnlk4aWXXipMnz698C//8i+FUaNG1cte9bHb/vvvX/jTn/5UePnllwtHH310ve5WKHy5/Z566qnCnnvuWXjkkUcKr7/+emHs2LGFvffeu/DOO+8UCoVC4fe//32hV69ehf/zf/5PYfr06YVBgwYVrrzyykax28dKdT+lmLuV+j5Ksfcr9f2U+vi+rM/7KOLoU2bPnl3o0qVLYfHixdXH/vu//7vQr1+/wp///OfCXnvtVfjggw+qP/Zv//ZvNf4CufHGGwt77LFH4cgjj6zxj8b7779fGDZsWGHfffctHHTQQSWJo2LtVigUCvvvv3/hscceq35/4cKFhS5duhTmzZtXvIU+oZi7XXHFFYUpU6ZUv3/fffcV/uVf/qWI23xWMff72GmnnVY46aST6j2Oirnbr371q8KJJ55Y/CU+RzF3O+aYYwo33nhj9fsvvvhi4YgjjiisXbu2iBvVVB/fl4VCoTB16tRCt27dCrNnzy7OIutQzN0GDx5c+PnPf179/v3331844ogjirhNTcXaraqqqtCzZ8/Cww8/XH1s2rRphW7duhWWL19e5K3+ny+z37nnnlu4/PLLa1zfYYcdVvjlL39ZKBQKhZNPPrnGn8XkyZMLe+65Z2HFihXFXKlaMXcr9f2UYu5W6vsohUJx9yv1/ZRi7vax+ryP4rS6T9lqq61y1113pUOHDjWOL1++PNOnT0/Xrl3TsmXL6uO9evXKtGnTqt9/7rnncvfdd+ewww6r8flvvvlmVq9enf/6r//KDjvsUNQdPk+xdquqqsqYMWOy//77f+Zrvv/++3W7xOco1m5J8oMf/CC9evVK8tHt+Oijj6Z3797FWeRzFHO/JJkwYUJWrlyZ448/vijzf5Fi7jZ79uzstNNOxRp9vYq12/Lly/PKK69k4MCB1cf23XffPProoykvLy/OMutQ7O/Lj1177bU54YQTsvPOO9fp/F+kmLu1bds2jz/+eJYtW5Zly5bliSeeyO677160XT6tWLu99957+eCDD9KjR4/qYxUVFfnwww/z8ssvF2eZdfgy+51xxhk57bTTPnOd77//fiorKzNjxozss88+1cf32muvfPjhh3nttdeKs8ynFGu3pPT3U4q1W0O4j5IU97Yr9f2UYu6W1P99lKb18lU2IltssUX69+9f/X5VVVXGjRuX/fbbL4sXL07Hjh1rXL59+/Y1zlt98MEHkySTJk2qcbnddtstt99+exEnX79i7dakSZPP/KVz//33Z8stt0xFRUVdr7FOxdrtky666KJMmDAh22+/fYYOHVrHG3yxYu733nvv5Zprrsm9996bGTNmFGmDz1fM3ebMmZO1a9fm+OOPz8KFC7PPPvtkxIgRn7nOYinWbvPnz0/y0W130kkn5c0338wBBxyQSy+9NFtssUWx1vmM+vjvburUqZk2bVquu+66Op7+ixVztwsvvDDDhg2rfv5bly5dcuuttxZjjXUq1m5t2rTJV77ylSxcuDC77LJLkuTtt99OkixZsqQou6zLl9mvW7duNT729NNP5/XXX89+++2Xf/zjH1m9enWNz2/atGnatm1bb89fKdZuSenvpxRrt4ZwHyUp7m33sVLdTynmbqW4j+KRo/UYM2ZMXnnllXz/+9/PypUr06xZsxofb9asWdasWVOi6b6cYu02ceLE3HPPPTn//PM/c531pRi7nXnmmfnlL3+Z7bffPmeeeWaqqqrqcuRaqcv9fvzjH+eb3/xmdt1112KMWmt1udvcuXOzfPnyjBgxImPHjs2iRYvyve99L5WVlcUYfb3qarePn2j7wx/+MGeeeWauv/76zJo1KxdeeGFR5t5Qxfjv7le/+lUGDhyYrbfeui5HrbW63O2NN97Itttum/vuuy933313Vq9enZ/85CfFGHuD1NVuTZs2zcCBA3PdddflnXfeyfvvv5+rr746TZs2zYcfflis8dfrn93vjTfeyIgRIzJ48OB069Ytq1atqr78hnx+fair3RqiYu3WEO6jJMXZr6HcT6nL3UpxH0UcfYExY8bkvvvuy5gxY9KlS5c0b978MzfmmjVrstlmm5Vown9esXabOHFizj333Jxyyin51re+VZcjb7Bi7bbLLrtkr732ytixYzNz5sx6fdWsT6rL/Z555plMmzat3h8J+zx1fds99thjGTduXHr16pV99tknN9xwQ1599dVMnz69GON/obrcrWnTjx70P+uss3LIIYekV69e+dGPfpQ//vGPWbhwYVHmX59i/He3du3aPPnkkznqqKPqetxaqcvdli9fnksvvTQXXXRR+vTpkwMOOCA//vGPM378+CxatKhYK3yuur7dRo4cmVatWuWggw7KgQcemL333jtt2rRJ69atizH+ev2z+82bNy+nnnpqdthhh4wePTpJ0rx58+rLf/rzW7RoUcQt1q0ud2toirVbQ7iPkhRvv4ZwP6UudyvVfRRx9DlGjRqVe++9N2PGjMk3vvGNJMnWW2+dd999t8bl3n333Xo7RaeuFGu3xx57LOecc05OPPHEXHLJJXU684aq693WrFmTxx9/PMuXL68+1qFDh7Rt27ZeTxP5WF3v97//9//OO++8k759+6Znz575wQ9+kAULFqRnz56ZMmVKUXb4PMX4vmzRokWNv4Dbt2+ftm3b1ntA1PVuW221VZKkc+fO1cc6deqUJCV5edpi/Z0ybdq0rF27NgcccECdzlsbdb3b3Llzs2LFiuy2227Vx7p27Zqqqqp6v+2Kcbu1b98+999/fyZNmpQ///nPOeWUU/L3v/8922+/fZ3Pvz7/7H6zZs3KKaeckm222SZ33XVX9d8hbdu2TfPmzWt8/tq1a7N06dLq/ybrS13v1pAUa7eGcB8lqfv9GtL9lLrerVT3UcTROtx000156KGHct111+WII46oPt6jR4/87W9/q35oPfnofPhPPvm0oSvWbs8//3wuvPDCfPvb385ll11W53NviGLs1qRJk1x88cX505/+VH1swYIFWbJkSb0+OTwpzn7/8R//kcceeywTJkzIhAkTMnz48HTs2DETJkzIHnvsUZQ91qUYuy1fvjz77rtvXnjhhepjCxcuzJIlS2pERbEVY7ftttsuHTt2rPEk8Dlz5qSsrCzbbbdd3S6wHsX8+3L69Onp1q1b9U/s61sxdvv4DsHs2bOrj82dOzdJ8tWvfrWuRl+vYt1uF1xwQZ599tm0bds2LVq0yFNPPZX27dtXPwepvvyz+y1atCinn356dtxxx9x99901HvFq0qRJunfvXuN3sEybNi1NmzatEbvFVozdGopi7dYQ7qMkxfu+bAj3U4qxW6nuo3hBhk+ZM2dObrnllpx11lnp1atXFi9eXP2x3r17Z9ttt82IESPy7//+7/njH/+Yl156KVdddVUJJ95wxdpt7dq1ueSSS7LvvvvmzDPPrHG9bdq0qZdzeou1W9OmTXPiiSfmuuuuyzbbbJPNNtsso0aNyiGHHFKv578Wa7/27dunffv2Nd5v2rRpdtxxx6LssS7F2q1169bp1atXrrrqqowaNSrl5eX50Y9+lP79+9fbk3CLtVtZWVmGDBmSG264IV/96lfTvn37XHHFFTn00EPr9SfYxf77ctasWfX+Q4iPFWu3bbbZJv37989ll12WH/7whykUCvnBD36QI444Iu3atSvmStWKebu1bds2Y8eOTceOHbNkyZKMGjUqZ511Vpo0qb+fxX6Z/a6++upUVVXlRz/6UVasWFH9y3lbtmyZVq1a5eSTT87ll1+eLl26pGPHjrniiitywgkn1NtpdcXcrdSKtVvz5s1Lfh+lmPu1atWq5PdTirVbqe6jiKNPefLJJ1NZWZlbb731M68eNHPmzNxyyy259NJLc+yxx2bHHXfMzTffXO8/qf1nFWu3l19+OQsWLMiCBQvSr1+/Gh+7//77q1+RqZiKebudd955KSsry7nnnpsVK1bksMMOy8iRI4uxxufyffnP7Xb11VfnJz/5Sc4666ysWbMmhxxySL3edsXc7fTTT8/q1atz4YUXZsWKFRkwYECuuOKKImzx+Yr9ffnuu+/W60tcf1Ixd7v22murvy/LyspyyCGH5KKLLirGGutUzN3OPffcXHnllTn55JPTsmXLDBkyJEOGDCnCFp/vn92vUChk4sSJWbVqVQ4//PAan3f22Wdn2LBhOeKII/LWW2/l8ssvz5o1a3LYYYflggsuaBS7lVqxduvfv3/J76Mkxb3tSn0/pbF9X5YVCoVCSb4yAABAA+I5RwAAABFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAGwkTjvvPNy0EEHZeXKlZ/52GmnnZaTTjopfq85AF+GOAJgo3DRRRfl/fffz2233Vbj+BNPPJHJkyfnhz/8YcrKyko0HQCNgTgCYKOw9dZbZ9iwYbn33nszf/78JMmqVavyk5/8JKeddlq6dOlS4gkB2NiJIwA2Gt/5zney4447ZsyYMUmSu+66K02aNMnQoUPz9ttv53vf+1569OiRAQMG5KabbkplZWX15/7617/O4Ycfnj322CN9+vTJlVdeWf3xiy++OBdffHGOOuqo9O3bN6+//nop1gOgxJqWegAA2FBNmzbN5Zdfnu985zuZOHFi7r777txwww1p3rx5zj777Oy22275zW9+k8WLF+fyyy9PWVlZhg4dmhdffDGjR4/OmDFj0rVr17z88su54IIL0rdv3xx22GFJkkceeSQ333xzOnTokJ122qm0iwJQEh45AmCjsu+++2bw4ME555xzcvDBB6d///554YUXsmDBgowaNSqdO3dOnz59ctFFF+X+++9PkrRs2TI/+tGPcthhh+WrX/1qDj/88HTt2jWzZs2qvt7u3btnwIAB2XPPPUu1GgAl5pEjADY63/ve9/Lb3/42Q4cOTZLMmTMnS5cuTa9evaovU1VVlVWrVmXJkiXZY489stlmm+WGG27I7NmzM3PmzPzP//xP+vXrV3357bffvt73AKBhEUcAbHSaN29e43/Xrl2bzp0755ZbbvnMZTfffPM888wzGTp0aI455pj0798/Q4cOzZVXXrnO6wRg0+W0OgA2ep06dcqCBQvSrl277Ljjjtlxxx3z5ptv5oYbbkhZWVl+/etf57jjjssPf/jDfOtb38rOO++cN954w+9FAqAGcQTARq9fv37Zfvvtc8EFF2TmzJmZMmVKLrvssrRo0SLl5eVp27Zt/vrXv2bmzJmZNWtWLr744ixevDhr1qwp9egANCDiCICNXnl5eW699dZUVVXlhBNOyLBhw3LQQQdl5MiRSZKzzz477du3z4knnpjTTjstzZs3z7/+67/m1VdfLfHkADQkZQXnFAAAAHjkCAAAIBFHAAAAScQRAABAEnEEAACQRBwBAAAkEUcAAABJxBEAAEAScQQAAJBEHAEAACQRRwAAAEnEEQAAQJLk/wMr6TQbLoIfGwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Published_date'] = pd.to_datetime(df['Published_date'])\n",
    "df['Year'] = df['Published_date'].dt.year\n",
    "df['Month'] = df['Published_date'].dt.strftime('%b')\n",
    "\n",
    "stats_by_year = df.groupby(['Year']).agg({'Views': 'sum', 'Likes': 'sum', 'Comments': 'sum'}).reset_index()\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "ax1 = sns.barplot(x='Year', y='Views', data=stats_by_year)\n",
    "plt.title('Total Views by Year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a bar plot showing the total views per month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# stats_by_month = df.groupby('Month').sum().reset_index()\n",
    "# stats_by_month = stats_by_month[['Month', 'Views', 'Likes','Comments']]\n",
    "stats_by_month = df.groupby('Month').agg({'Views': 'sum', 'Likes': 'sum', 'Comments': 'sum'}).reset_index()\n",
    "\n",
    "sort_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "stats_by_month.index = pd.CategoricalIndex(stats_by_month['Month'], categories=sort_order, ordered=True)\n",
    "stats_by_month = stats_by_month.sort_index()\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "ax1 = sns.barplot(x='Month', y='Views', data=stats_by_month)\n",
    "plt.title('Total Views by Month')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generates a word cloud visualization based on the frequency of words in video titles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'wordcloud'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[42], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mwordcloud\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m WordCloud\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot_cloud\u001b[39m(wordcloud):\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'wordcloud'"
     ]
    }
   ],
   "source": [
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_cloud(wordcloud):\n",
    "    plt.figure(figsize=(30, 20))\n",
    "    plt.imshow(wordcloud, interpolation='bilinear')  \n",
    "    plt.axis(\"off\")\n",
    "\n",
    "# Concatenate all video titles into a single string\n",
    "all_titles = ' '.join(df['Title'])\n",
    "\n",
    "# Generate word cloud\n",
    "wordcloud = WordCloud(width=2000, height=1000, random_state=1, background_color='black',\n",
    "                      colormap='viridis', collocations=False).generate(all_titles)\n",
    "\n",
    "plot_cloud(wordcloud)\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Descriptive statistics for the Number of Tags in a video and Creating a histogram showing The Distribution of the Tag counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Number_of_Tags\n",
      "count     1515.000000\n",
      "mean        18.556436\n",
      "std          9.776834\n",
      "min          1.000000\n",
      "25%         12.000000\n",
      "50%         17.000000\n",
      "75%         25.000000\n",
      "max         61.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def count_tags(tags):\n",
    "    # Split the tags string into a list\n",
    "    tag_list = tags.strip(\"[]\").replace(\"'\", \"\").split(\", \")\n",
    "    # Count the number of tags\n",
    "    return len(tag_list)\n",
    "\n",
    "df_copy=df.copy()\n",
    "\n",
    "# Apply the count_tags function to each element in the 'Tags' column\n",
    "df_copy['Number_of_Tags'] = df_copy['Tags'].apply(count_tags)\n",
    "\n",
    "#Descriptive statistics for numeric columns\n",
    "print(df_copy[['Number_of_Tags']].describe())   \n",
    "\n",
    "# visualisation of number of tags\n",
    "plt.figure(figsize=(10, 6))\n",
    "pink = \"#CF598C\"\n",
    "sns.histplot(data=df_copy, x='Number_of_Tags', bins=range(df_copy['Number_of_Tags'].min(), df_copy['Number_of_Tags'].max() + 1), color=pink)\n",
    "plt.title(\"Distribution of Number of Tags per Video\")\n",
    "plt.xlabel(\"Number of Tags\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}