YouTube_Analytics_2023.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "from matplotlib import ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>rank</th>\n",
       "      <th>Youtuber</th>\n",
       "      <th>subscribers</th>\n",
       "      <th>video views</th>\n",
       "      <th>category</th>\n",
       "      <th>Title</th>\n",
       "      <th>uploads</th>\n",
       "      <th>Country</th>\n",
       "      <th>Abbreviation</th>\n",
       "      <th>channel_type</th>\n",
       "      <th>...</th>\n",
       "      <th>subscribers_for_last_30_days</th>\n",
       "      <th>created_year</th>\n",
       "      <th>created_month</th>\n",
       "      <th>created_date</th>\n",
       "      <th>Gross tertiary education enrollment (%)</th>\n",
       "      <th>Population</th>\n",
       "      <th>Unemployment rate</th>\n",
       "      <th>Urban_population</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>T-Series</td>\n",
       "      <td>245000000</td>\n",
       "      <td>2.280000e+11</td>\n",
       "      <td>Music</td>\n",
       "      <td>T-Series</td>\n",
       "      <td>20082</td>\n",
       "      <td>India</td>\n",
       "      <td>IN</td>\n",
       "      <td>Music</td>\n",
       "      <td>...</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Mar</td>\n",
       "      <td>13.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.366418e+09</td>\n",
       "      <td>5.36</td>\n",
       "      <td>471031528.0</td>\n",
       "      <td>20.593684</td>\n",
       "      <td>78.962880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>YouTube Movies</td>\n",
       "      <td>170000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Film &amp; Animation</td>\n",
       "      <td>youtubemovies</td>\n",
       "      <td>1</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>Games</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Mar</td>\n",
       "      <td>5.0</td>\n",
       "      <td>88.2</td>\n",
       "      <td>3.282395e+08</td>\n",
       "      <td>14.70</td>\n",
       "      <td>270663028.0</td>\n",
       "      <td>37.090240</td>\n",
       "      <td>-95.712891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>MrBeast</td>\n",
       "      <td>166000000</td>\n",
       "      <td>2.836884e+10</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>MrBeast</td>\n",
       "      <td>741</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>...</td>\n",
       "      <td>8000000.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>20.0</td>\n",
       "      <td>88.2</td>\n",
       "      <td>3.282395e+08</td>\n",
       "      <td>14.70</td>\n",
       "      <td>270663028.0</td>\n",
       "      <td>37.090240</td>\n",
       "      <td>-95.712891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Cocomelon - Nursery Rhymes</td>\n",
       "      <td>162000000</td>\n",
       "      <td>1.640000e+11</td>\n",
       "      <td>Education</td>\n",
       "      <td>Cocomelon - Nursery Rhymes</td>\n",
       "      <td>966</td>\n",
       "      <td>United States</td>\n",
       "      <td>US</td>\n",
       "      <td>Education</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sep</td>\n",
       "      <td>1.0</td>\n",
       "      <td>88.2</td>\n",
       "      <td>3.282395e+08</td>\n",
       "      <td>14.70</td>\n",
       "      <td>270663028.0</td>\n",
       "      <td>37.090240</td>\n",
       "      <td>-95.712891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>SET India</td>\n",
       "      <td>159000000</td>\n",
       "      <td>1.480000e+11</td>\n",
       "      <td>Shows</td>\n",
       "      <td>SET India</td>\n",
       "      <td>116536</td>\n",
       "      <td>India</td>\n",
       "      <td>IN</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>...</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sep</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.366418e+09</td>\n",
       "      <td>5.36</td>\n",
       "      <td>471031528.0</td>\n",
       "      <td>20.593684</td>\n",
       "      <td>78.962880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank                    Youtuber  subscribers   video views  \\\n",
       "0     1                    T-Series    245000000  2.280000e+11   \n",
       "1     2              YouTube Movies    170000000  0.000000e+00   \n",
       "2     3                     MrBeast    166000000  2.836884e+10   \n",
       "3     4  Cocomelon - Nursery Rhymes    162000000  1.640000e+11   \n",
       "4     5                   SET India    159000000  1.480000e+11   \n",
       "\n",
       "           category                       Title  uploads        Country  \\\n",
       "0             Music                    T-Series    20082          India   \n",
       "1  Film & Animation               youtubemovies        1  United States   \n",
       "2     Entertainment                     MrBeast      741  United States   \n",
       "3         Education  Cocomelon - Nursery Rhymes      966  United States   \n",
       "4             Shows                   SET India   116536          India   \n",
       "\n",
       "  Abbreviation   channel_type  ...  subscribers_for_last_30_days  \\\n",
       "0           IN          Music  ...                     2000000.0   \n",
       "1           US          Games  ...                           NaN   \n",
       "2           US  Entertainment  ...                     8000000.0   \n",
       "3           US      Education  ...                     1000000.0   \n",
       "4           IN  Entertainment  ...                     1000000.0   \n",
       "\n",
       "   created_year  created_month  created_date  \\\n",
       "0        2006.0            Mar          13.0   \n",
       "1        2006.0            Mar           5.0   \n",
       "2        2012.0            Feb          20.0   \n",
       "3        2006.0            Sep           1.0   \n",
       "4        2006.0            Sep          20.0   \n",
       "\n",
       "   Gross tertiary education enrollment (%)    Population  Unemployment rate  \\\n",
       "0                                     28.1  1.366418e+09               5.36   \n",
       "1                                     88.2  3.282395e+08              14.70   \n",
       "2                                     88.2  3.282395e+08              14.70   \n",
       "3                                     88.2  3.282395e+08              14.70   \n",
       "4                                     28.1  1.366418e+09               5.36   \n",
       "\n",
       "   Urban_population   Latitude  Longitude  \n",
       "0       471031528.0  20.593684  78.962880  \n",
       "1       270663028.0  37.090240 -95.712891  \n",
       "2       270663028.0  37.090240 -95.712891  \n",
       "3       270663028.0  37.090240 -95.712891  \n",
       "4       471031528.0  20.593684  78.962880  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"DATA/Global_YouTube_Statistics.csv\", encoding=\"ISO-8859-1\")\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 995 entries, 0 to 994\n",
      "Data columns (total 28 columns):\n",
      " #   Column                                   Non-Null Count  Dtype  \n",
      "---  ------                                   --------------  -----  \n",
      " 0   rank                                     995 non-null    int64  \n",
      " 1   Youtuber                                 995 non-null    object \n",
      " 2   subscribers                              995 non-null    int64  \n",
      " 3   video views                              995 non-null    float64\n",
      " 4   category                                 949 non-null    object \n",
      " 5   Title                                    995 non-null    object \n",
      " 6   uploads                                  995 non-null    int64  \n",
      " 7   Country                                  873 non-null    object \n",
      " 8   Abbreviation                             873 non-null    object \n",
      " 9   channel_type                             965 non-null    object \n",
      " 10  video_views_rank                         994 non-null    float64\n",
      " 11  country_rank                             879 non-null    float64\n",
      " 12  channel_type_rank                        962 non-null    float64\n",
      " 13  video_views_for_the_last_30_days         939 non-null    float64\n",
      " 14  lowest_monthly_earnings                  995 non-null    float64\n",
      " 15  highest_monthly_earnings                 995 non-null    float64\n",
      " 16  lowest_yearly_earnings                   995 non-null    float64\n",
      " 17  highest_yearly_earnings                  995 non-null    float64\n",
      " 18  subscribers_for_last_30_days             658 non-null    float64\n",
      " 19  created_year                             990 non-null    float64\n",
      " 20  created_month                            990 non-null    object \n",
      " 21  created_date                             990 non-null    float64\n",
      " 22  Gross tertiary education enrollment (%)  872 non-null    float64\n",
      " 23  Population                               872 non-null    float64\n",
      " 24  Unemployment rate                        872 non-null    float64\n",
      " 25  Urban_population                         872 non-null    float64\n",
      " 26  Latitude                                 872 non-null    float64\n",
      " 27  Longitude                                872 non-null    float64\n",
      "dtypes: float64(18), int64(3), object(7)\n",
      "memory usage: 217.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['rank', 'Youtuber', 'subscribers', 'video views', 'category', 'Title',\n",
       "       'uploads', 'Country', 'Abbreviation', 'channel_type',\n",
       "       'video_views_rank', 'country_rank', 'channel_type_rank',\n",
       "       'video_views_for_the_last_30_days', 'lowest_monthly_earnings',\n",
       "       'highest_monthly_earnings', 'lowest_yearly_earnings',\n",
       "       'highest_yearly_earnings', 'subscribers_for_last_30_days',\n",
       "       'created_year', 'created_month', 'created_date',\n",
       "       'Gross tertiary education enrollment (%)', 'Population',\n",
       "       'Unemployment rate', 'Urban_population', 'Latitude', 'Longitude'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>rank</th>\n",
       "      <th>Youtuber</th>\n",
       "      <th>subscribers</th>\n",
       "      <th>video views</th>\n",
       "      <th>category</th>\n",
       "      <th>Title</th>\n",
       "      <th>uploads</th>\n",
       "      <th>Country</th>\n",
       "      <th>Abbreviation</th>\n",
       "      <th>channel_type</th>\n",
       "      <th>...</th>\n",
       "      <th>subscribers_for_last_30_days</th>\n",
       "      <th>created_year</th>\n",
       "      <th>created_month</th>\n",
       "      <th>created_date</th>\n",
       "      <th>Gross tertiary education enrollment (%)</th>\n",
       "      <th>Population</th>\n",
       "      <th>Unemployment rate</th>\n",
       "      <th>Urban_population</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>991</td>\n",
       "      <td>Natan por Aï¿</td>\n",
       "      <td>12300000</td>\n",
       "      <td>9.029610e+09</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Natan por Aï¿</td>\n",
       "      <td>1200</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>BR</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>...</td>\n",
       "      <td>700000.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>Feb</td>\n",
       "      <td>12.0</td>\n",
       "      <td>51.3</td>\n",
       "      <td>2.125594e+08</td>\n",
       "      <td>12.08</td>\n",
       "      <td>183241641.0</td>\n",
       "      <td>-14.235004</td>\n",
       "      <td>-51.925280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>992</td>\n",
       "      <td>Free Fire India Official</td>\n",
       "      <td>12300000</td>\n",
       "      <td>1.674410e+09</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>Free Fire India Official</td>\n",
       "      <td>1500</td>\n",
       "      <td>India</td>\n",
       "      <td>IN</td>\n",
       "      <td>Games</td>\n",
       "      <td>...</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>Sep</td>\n",
       "      <td>14.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.366418e+09</td>\n",
       "      <td>5.36</td>\n",
       "      <td>471031528.0</td>\n",
       "      <td>20.593684</td>\n",
       "      <td>78.962880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>993</td>\n",
       "      <td>Panda</td>\n",
       "      <td>12300000</td>\n",
       "      <td>2.214684e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HybridPanda</td>\n",
       "      <td>2452</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GB</td>\n",
       "      <td>Games</td>\n",
       "      <td>...</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>Sep</td>\n",
       "      <td>11.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>6.683440e+07</td>\n",
       "      <td>3.85</td>\n",
       "      <td>55908316.0</td>\n",
       "      <td>55.378051</td>\n",
       "      <td>-3.435973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>994</td>\n",
       "      <td>RobTopGames</td>\n",
       "      <td>12300000</td>\n",
       "      <td>3.741235e+08</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>RobTopGames</td>\n",
       "      <td>39</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>SE</td>\n",
       "      <td>Games</td>\n",
       "      <td>...</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>May</td>\n",
       "      <td>9.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1.028545e+07</td>\n",
       "      <td>6.48</td>\n",
       "      <td>9021165.0</td>\n",
       "      <td>60.128161</td>\n",
       "      <td>18.643501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>995</td>\n",
       "      <td>Make Joke Of</td>\n",
       "      <td>12300000</td>\n",
       "      <td>2.129774e+09</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Make Joke Of</td>\n",
       "      <td>62</td>\n",
       "      <td>India</td>\n",
       "      <td>IN</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>...</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>Aug</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.366418e+09</td>\n",
       "      <td>5.36</td>\n",
       "      <td>471031528.0</td>\n",
       "      <td>20.593684</td>\n",
       "      <td>78.962880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     rank                  Youtuber  subscribers   video views  \\\n",
       "990   991             Natan por Aï¿     12300000  9.029610e+09   \n",
       "991   992  Free Fire India Official     12300000  1.674410e+09   \n",
       "992   993                     Panda     12300000  2.214684e+09   \n",
       "993   994               RobTopGames     12300000  3.741235e+08   \n",
       "994   995              Make Joke Of     12300000  2.129774e+09   \n",
       "\n",
       "           category                     Title  uploads         Country  \\\n",
       "990          Sports             Natan por Aï¿     1200          Brazil   \n",
       "991  People & Blogs  Free Fire India Official     1500           India   \n",
       "992             NaN               HybridPanda     2452  United Kingdom   \n",
       "993          Gaming               RobTopGames       39          Sweden   \n",
       "994          Comedy              Make Joke Of       62           India   \n",
       "\n",
       "    Abbreviation   channel_type  ...  subscribers_for_last_30_days  \\\n",
       "990           BR  Entertainment  ...                      700000.0   \n",
       "991           IN          Games  ...                      300000.0   \n",
       "992           GB          Games  ...                        1000.0   \n",
       "993           SE          Games  ...                      100000.0   \n",
       "994           IN         Comedy  ...                      100000.0   \n",
       "\n",
       "     created_year  created_month  created_date  \\\n",
       "990        2017.0            Feb          12.0   \n",
       "991        2018.0            Sep          14.0   \n",
       "992        2006.0            Sep          11.0   \n",
       "993        2012.0            May           9.0   \n",
       "994        2017.0            Aug           1.0   \n",
       "\n",
       "     Gross tertiary education enrollment (%)    Population  Unemployment rate  \\\n",
       "990                                     51.3  2.125594e+08              12.08   \n",
       "991                                     28.1  1.366418e+09               5.36   \n",
       "992                                     60.0  6.683440e+07               3.85   \n",
       "993                                     67.0  1.028545e+07               6.48   \n",
       "994                                     28.1  1.366418e+09               5.36   \n",
       "\n",
       "     Urban_population   Latitude  Longitude  \n",
       "990       183241641.0 -14.235004 -51.925280  \n",
       "991       471031528.0  20.593684  78.962880  \n",
       "992        55908316.0  55.378051  -3.435973  \n",
       "993         9021165.0  60.128161  18.643501  \n",
       "994       471031528.0  20.593684  78.962880  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>rank</th>\n",
       "      <th>subscribers</th>\n",
       "      <th>video views</th>\n",
       "      <th>uploads</th>\n",
       "      <th>video_views_rank</th>\n",
       "      <th>country_rank</th>\n",
       "      <th>channel_type_rank</th>\n",
       "      <th>video_views_for_the_last_30_days</th>\n",
       "      <th>lowest_monthly_earnings</th>\n",
       "      <th>highest_monthly_earnings</th>\n",
       "      <th>...</th>\n",
       "      <th>highest_yearly_earnings</th>\n",
       "      <th>subscribers_for_last_30_days</th>\n",
       "      <th>created_year</th>\n",
       "      <th>created_date</th>\n",
       "      <th>Gross tertiary education enrollment (%)</th>\n",
       "      <th>Population</th>\n",
       "      <th>Unemployment rate</th>\n",
       "      <th>Urban_population</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>995.00000</td>\n",
       "      <td>9.950000e+02</td>\n",
       "      <td>9.950000e+02</td>\n",
       "      <td>995.000000</td>\n",
       "      <td>9.940000e+02</td>\n",
       "      <td>879.000000</td>\n",
       "      <td>962.000000</td>\n",
       "      <td>9.390000e+02</td>\n",
       "      <td>995.000000</td>\n",
       "      <td>9.950000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>9.950000e+02</td>\n",
       "      <td>6.580000e+02</td>\n",
       "      <td>990.000000</td>\n",
       "      <td>990.000000</td>\n",
       "      <td>872.000000</td>\n",
       "      <td>8.720000e+02</td>\n",
       "      <td>872.000000</td>\n",
       "      <td>8.720000e+02</td>\n",
       "      <td>872.000000</td>\n",
       "      <td>872.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>498.00000</td>\n",
       "      <td>2.298241e+07</td>\n",
       "      <td>1.103954e+10</td>\n",
       "      <td>9187.125628</td>\n",
       "      <td>5.542489e+05</td>\n",
       "      <td>386.053470</td>\n",
       "      <td>745.719335</td>\n",
       "      <td>1.756103e+08</td>\n",
       "      <td>36886.148281</td>\n",
       "      <td>5.898078e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>7.081814e+06</td>\n",
       "      <td>3.490791e+05</td>\n",
       "      <td>2012.630303</td>\n",
       "      <td>15.746465</td>\n",
       "      <td>63.627752</td>\n",
       "      <td>4.303873e+08</td>\n",
       "      <td>9.279278</td>\n",
       "      <td>2.242150e+08</td>\n",
       "      <td>26.632783</td>\n",
       "      <td>-14.128146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>287.37606</td>\n",
       "      <td>1.752611e+07</td>\n",
       "      <td>1.411084e+10</td>\n",
       "      <td>34151.352254</td>\n",
       "      <td>1.362782e+06</td>\n",
       "      <td>1232.244746</td>\n",
       "      <td>1944.386561</td>\n",
       "      <td>4.163782e+08</td>\n",
       "      <td>71858.724092</td>\n",
       "      <td>1.148622e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.379704e+07</td>\n",
       "      <td>6.143554e+05</td>\n",
       "      <td>4.512503</td>\n",
       "      <td>8.777520</td>\n",
       "      <td>26.106893</td>\n",
       "      <td>4.727947e+08</td>\n",
       "      <td>4.888354</td>\n",
       "      <td>1.546874e+08</td>\n",
       "      <td>20.560533</td>\n",
       "      <td>84.760809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.230000e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>2.025060e+05</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>3.558800e+04</td>\n",
       "      <td>-38.416097</td>\n",
       "      <td>-172.104629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>249.50000</td>\n",
       "      <td>1.450000e+07</td>\n",
       "      <td>4.288145e+09</td>\n",
       "      <td>194.500000</td>\n",
       "      <td>3.230000e+02</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.013750e+07</td>\n",
       "      <td>2700.000000</td>\n",
       "      <td>4.350000e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>5.217500e+05</td>\n",
       "      <td>1.000000e+05</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>36.300000</td>\n",
       "      <td>8.335541e+07</td>\n",
       "      <td>5.270000</td>\n",
       "      <td>5.590832e+07</td>\n",
       "      <td>20.593684</td>\n",
       "      <td>-95.712891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>498.00000</td>\n",
       "      <td>1.770000e+07</td>\n",
       "      <td>7.760820e+09</td>\n",
       "      <td>729.000000</td>\n",
       "      <td>9.155000e+02</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>6.408500e+07</td>\n",
       "      <td>13300.000000</td>\n",
       "      <td>2.127000e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.600000e+06</td>\n",
       "      <td>2.000000e+05</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>3.282395e+08</td>\n",
       "      <td>9.365000</td>\n",
       "      <td>2.706630e+08</td>\n",
       "      <td>37.090240</td>\n",
       "      <td>-51.925280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>746.50000</td>\n",
       "      <td>2.460000e+07</td>\n",
       "      <td>1.355470e+10</td>\n",
       "      <td>2667.500000</td>\n",
       "      <td>3.584500e+03</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>139.750000</td>\n",
       "      <td>1.688265e+08</td>\n",
       "      <td>37900.000000</td>\n",
       "      <td>6.068000e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>7.300000e+06</td>\n",
       "      <td>4.000000e+05</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>88.200000</td>\n",
       "      <td>3.282395e+08</td>\n",
       "      <td>14.700000</td>\n",
       "      <td>2.706630e+08</td>\n",
       "      <td>37.090240</td>\n",
       "      <td>78.962880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>995.00000</td>\n",
       "      <td>2.450000e+08</td>\n",
       "      <td>2.280000e+11</td>\n",
       "      <td>301308.000000</td>\n",
       "      <td>4.057944e+06</td>\n",
       "      <td>7741.000000</td>\n",
       "      <td>7741.000000</td>\n",
       "      <td>6.589000e+09</td>\n",
       "      <td>850900.000000</td>\n",
       "      <td>1.360000e+07</td>\n",
       "      <td>...</td>\n",
       "      <td>1.634000e+08</td>\n",
       "      <td>8.000000e+06</td>\n",
       "      <td>2022.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>113.100000</td>\n",
       "      <td>1.397715e+09</td>\n",
       "      <td>14.720000</td>\n",
       "      <td>8.429340e+08</td>\n",
       "      <td>61.924110</td>\n",
       "      <td>138.252924</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            rank   subscribers   video views        uploads  video_views_rank  \\\n",
       "count  995.00000  9.950000e+02  9.950000e+02     995.000000      9.940000e+02   \n",
       "mean   498.00000  2.298241e+07  1.103954e+10    9187.125628      5.542489e+05   \n",
       "std    287.37606  1.752611e+07  1.411084e+10   34151.352254      1.362782e+06   \n",
       "min      1.00000  1.230000e+07  0.000000e+00       0.000000      1.000000e+00   \n",
       "25%    249.50000  1.450000e+07  4.288145e+09     194.500000      3.230000e+02   \n",
       "50%    498.00000  1.770000e+07  7.760820e+09     729.000000      9.155000e+02   \n",
       "75%    746.50000  2.460000e+07  1.355470e+10    2667.500000      3.584500e+03   \n",
       "max    995.00000  2.450000e+08  2.280000e+11  301308.000000      4.057944e+06   \n",
       "\n",
       "       country_rank  channel_type_rank  video_views_for_the_last_30_days  \\\n",
       "count    879.000000         962.000000                      9.390000e+02   \n",
       "mean     386.053470         745.719335                      1.756103e+08   \n",
       "std     1232.244746        1944.386561                      4.163782e+08   \n",
       "min        1.000000           1.000000                      1.000000e+00   \n",
       "25%       11.000000          27.000000                      2.013750e+07   \n",
       "50%       51.000000          65.500000                      6.408500e+07   \n",
       "75%      123.000000         139.750000                      1.688265e+08   \n",
       "max     7741.000000        7741.000000                      6.589000e+09   \n",
       "\n",
       "       lowest_monthly_earnings  highest_monthly_earnings  ...  \\\n",
       "count               995.000000              9.950000e+02  ...   \n",
       "mean              36886.148281              5.898078e+05  ...   \n",
       "std               71858.724092              1.148622e+06  ...   \n",
       "min                   0.000000              0.000000e+00  ...   \n",
       "25%                2700.000000              4.350000e+04  ...   \n",
       "50%               13300.000000              2.127000e+05  ...   \n",
       "75%               37900.000000              6.068000e+05  ...   \n",
       "max              850900.000000              1.360000e+07  ...   \n",
       "\n",
       "       highest_yearly_earnings  subscribers_for_last_30_days  created_year  \\\n",
       "count             9.950000e+02                  6.580000e+02    990.000000   \n",
       "mean              7.081814e+06                  3.490791e+05   2012.630303   \n",
       "std               1.379704e+07                  6.143554e+05      4.512503   \n",
       "min               0.000000e+00                  1.000000e+00   1970.000000   \n",
       "25%               5.217500e+05                  1.000000e+05   2009.000000   \n",
       "50%               2.600000e+06                  2.000000e+05   2013.000000   \n",
       "75%               7.300000e+06                  4.000000e+05   2016.000000   \n",
       "max               1.634000e+08                  8.000000e+06   2022.000000   \n",
       "\n",
       "       created_date  Gross tertiary education enrollment (%)    Population  \\\n",
       "count    990.000000                               872.000000  8.720000e+02   \n",
       "mean      15.746465                                63.627752  4.303873e+08   \n",
       "std        8.777520                                26.106893  4.727947e+08   \n",
       "min        1.000000                                 7.600000  2.025060e+05   \n",
       "25%        8.000000                                36.300000  8.335541e+07   \n",
       "50%       16.000000                                68.000000  3.282395e+08   \n",
       "75%       23.000000                                88.200000  3.282395e+08   \n",
       "max       31.000000                               113.100000  1.397715e+09   \n",
       "\n",
       "       Unemployment rate  Urban_population    Latitude   Longitude  \n",
       "count         872.000000      8.720000e+02  872.000000  872.000000  \n",
       "mean            9.279278      2.242150e+08   26.632783  -14.128146  \n",
       "std             4.888354      1.546874e+08   20.560533   84.760809  \n",
       "min             0.750000      3.558800e+04  -38.416097 -172.104629  \n",
       "25%             5.270000      5.590832e+07   20.593684  -95.712891  \n",
       "50%             9.365000      2.706630e+08   37.090240  -51.925280  \n",
       "75%            14.700000      2.706630e+08   37.090240   78.962880  \n",
       "max            14.720000      8.429340e+08   61.924110  138.252924  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(995, 28)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rank                                         0\n",
       "Youtuber                                     0\n",
       "subscribers                                  0\n",
       "video views                                  0\n",
       "category                                    46\n",
       "Title                                        0\n",
       "uploads                                      0\n",
       "Country                                    122\n",
       "Abbreviation                               122\n",
       "channel_type                                30\n",
       "video_views_rank                             1\n",
       "country_rank                               116\n",
       "channel_type_rank                           33\n",
       "video_views_for_the_last_30_days            56\n",
       "lowest_monthly_earnings                      0\n",
       "highest_monthly_earnings                     0\n",
       "lowest_yearly_earnings                       0\n",
       "highest_yearly_earnings                      0\n",
       "subscribers_for_last_30_days               337\n",
       "created_year                                 5\n",
       "created_month                                5\n",
       "created_date                                 5\n",
       "Gross tertiary education enrollment (%)    123\n",
       "Population                                 123\n",
       "Unemployment rate                          123\n",
       "Urban_population                           123\n",
       "Latitude                                   123\n",
       "Longitude                                  123\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Music', 'Film & Animation', 'Entertainment', 'Education', 'Shows',\n",
       "       nan, 'People & Blogs', 'Gaming', 'Sports', 'Howto & Style',\n",
       "       'News & Politics', 'Comedy', 'Trailers', 'Nonprofits & Activism',\n",
       "       'Science & Technology', 'Movies', 'Pets & Animals',\n",
       "       'Autos & Vehicles', 'Travel & Events'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['category'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>rank</th>\n",
       "      <th>Youtuber</th>\n",
       "      <th>subscribers</th>\n",
       "      <th>video views</th>\n",
       "      <th>category</th>\n",
       "      <th>Title</th>\n",
       "      <th>uploads</th>\n",
       "      <th>Country</th>\n",
       "      <th>Abbreviation</th>\n",
       "      <th>channel_type</th>\n",
       "      <th>...</th>\n",
       "      <th>subscribers_for_last_30_days</th>\n",
       "      <th>created_year</th>\n",
       "      <th>created_month</th>\n",
       "      <th>created_date</th>\n",
       "      <th>Gross tertiary education enrollment (%)</th>\n",
       "      <th>Population</th>\n",
       "      <th>Unemployment rate</th>\n",
       "      <th>Urban_population</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [rank, Youtuber, subscribers, video views, category, Title, uploads, Country, Abbreviation, channel_type, video_views_rank, country_rank, channel_type_rank, video_views_for_the_last_30_days, lowest_monthly_earnings, highest_monthly_earnings, lowest_yearly_earnings, highest_yearly_earnings, subscribers_for_last_30_days, created_year, created_month, created_date, Gross tertiary education enrollment (%), Population, Unemployment rate, Urban_population, Latitude, Longitude]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 28 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['category'].isnull()].head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 554 entries, 0 to 994\n",
      "Data columns (total 23 columns):\n",
      " #   Column                                   Non-Null Count  Dtype  \n",
      "---  ------                                   --------------  -----  \n",
      " 0   rank                                     554 non-null    int64  \n",
      " 1   Youtuber                                 554 non-null    object \n",
      " 2   subscribers                              554 non-null    int64  \n",
      " 3   video views                              554 non-null    float64\n",
      " 4   category                                 554 non-null    object \n",
      " 5   Title                                    554 non-null    object \n",
      " 6   uploads                                  554 non-null    int64  \n",
      " 7   Country                                  554 non-null    object \n",
      " 8   channel_type                             554 non-null    object \n",
      " 9   video_views_rank                         554 non-null    float64\n",
      " 10  country_rank                             554 non-null    float64\n",
      " 11  channel_type_rank                        554 non-null    float64\n",
      " 12  video_views_for_the_last_30_days         554 non-null    float64\n",
      " 13  lowest_monthly_earnings                  554 non-null    float64\n",
      " 14  highest_monthly_earnings                 554 non-null    float64\n",
      " 15  lowest_yearly_earnings                   554 non-null    float64\n",
      " 16  highest_yearly_earnings                  554 non-null    float64\n",
      " 17  subscribers_for_last_30_days             554 non-null    float64\n",
      " 18  created_year                             554 non-null    float64\n",
      " 19  Gross tertiary education enrollment (%)  554 non-null    float64\n",
      " 20  Population                               554 non-null    float64\n",
      " 21  Unemployment rate                        554 non-null    float64\n",
      " 22  Urban_population                         554 non-null    float64\n",
      "dtypes: float64(15), int64(3), object(5)\n",
      "memory usage: 103.9+ KB\n"
     ]
    }
   ],
   "source": [
    "dropped_features = [\n",
    "    'Abbreviation', 'created_month', 'created_date', 'Latitude', 'Longitude'\n",
    "]\n",
    "data = data.drop(data[dropped_features], axis=1)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
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       "    .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>subscribers</th>\n",
       "      <th>video views</th>\n",
       "      <th>uploads</th>\n",
       "      <th>lowest_monthly_earnings</th>\n",
       "      <th>highest_monthly_earnings</th>\n",
       "      <th>lowest_yearly_earnings</th>\n",
       "      <th>highest_yearly_earnings</th>\n",
       "      <th>video_views_for_the_last_30_days</th>\n",
       "      <th>subscribers_for_last_30_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>subscribers</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850776</td>\n",
       "      <td>0.074791</td>\n",
       "      <td>0.534713</td>\n",
       "      <td>0.534388</td>\n",
       "      <td>0.534883</td>\n",
       "      <td>0.534721</td>\n",
       "      <td>0.321182</td>\n",
       "      <td>0.361800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>video views</th>\n",
       "      <td>0.850776</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.147477</td>\n",
       "      <td>0.637991</td>\n",
       "      <td>0.637376</td>\n",
       "      <td>0.638861</td>\n",
       "      <td>0.638019</td>\n",
       "      <td>0.360970</td>\n",
       "      <td>0.203693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uploads</th>\n",
       "      <td>0.074791</td>\n",
       "      <td>0.147477</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.155557</td>\n",
       "      <td>0.156062</td>\n",
       "      <td>0.156639</td>\n",
       "      <td>0.155540</td>\n",
       "      <td>0.079308</td>\n",
       "      <td>0.009067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lowest_monthly_earnings</th>\n",
       "      <td>0.534713</td>\n",
       "      <td>0.637991</td>\n",
       "      <td>0.155557</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999937</td>\n",
       "      <td>0.999914</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.585838</td>\n",
       "      <td>0.616795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highest_monthly_earnings</th>\n",
       "      <td>0.534388</td>\n",
       "      <td>0.637376</td>\n",
       "      <td>0.156062</td>\n",
       "      <td>0.999937</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999832</td>\n",
       "      <td>0.999935</td>\n",
       "      <td>0.585788</td>\n",
       "      <td>0.617150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lowest_yearly_earnings</th>\n",
       "      <td>0.534883</td>\n",
       "      <td>0.638861</td>\n",
       "      <td>0.156639</td>\n",
       "      <td>0.999914</td>\n",
       "      <td>0.999832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.585758</td>\n",
       "      <td>0.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highest_yearly_earnings</th>\n",
       "      <td>0.534721</td>\n",
       "      <td>0.638019</td>\n",
       "      <td>0.155540</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.999935</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.585837</td>\n",
       "      <td>0.616816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>video_views_for_the_last_30_days</th>\n",
       "      <td>0.321182</td>\n",
       "      <td>0.360970</td>\n",
       "      <td>0.079308</td>\n",
       "      <td>0.585838</td>\n",
       "      <td>0.585788</td>\n",
       "      <td>0.585758</td>\n",
       "      <td>0.585837</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.366320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subscribers_for_last_30_days</th>\n",
       "      <td>0.361800</td>\n",
       "      <td>0.203693</td>\n",
       "      <td>0.009067</td>\n",
       "      <td>0.616795</td>\n",
       "      <td>0.617150</td>\n",
       "      <td>0.615874</td>\n",
       "      <td>0.616816</td>\n",
       "      <td>0.366320</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  subscribers  video views   uploads  \\\n",
       "subscribers                          1.000000     0.850776  0.074791   \n",
       "video views                          0.850776     1.000000  0.147477   \n",
       "uploads                              0.074791     0.147477  1.000000   \n",
       "lowest_monthly_earnings              0.534713     0.637991  0.155557   \n",
       "highest_monthly_earnings             0.534388     0.637376  0.156062   \n",
       "lowest_yearly_earnings               0.534883     0.638861  0.156639   \n",
       "highest_yearly_earnings              0.534721     0.638019  0.155540   \n",
       "video_views_for_the_last_30_days     0.321182     0.360970  0.079308   \n",
       "subscribers_for_last_30_days         0.361800     0.203693  0.009067   \n",
       "\n",
       "                                  lowest_monthly_earnings  \\\n",
       "subscribers                                      0.534713   \n",
       "video views                                      0.637991   \n",
       "uploads                                          0.155557   \n",
       "lowest_monthly_earnings                          1.000000   \n",
       "highest_monthly_earnings                         0.999937   \n",
       "lowest_yearly_earnings                           0.999914   \n",
       "highest_yearly_earnings                          0.999998   \n",
       "video_views_for_the_last_30_days                 0.585838   \n",
       "subscribers_for_last_30_days                     0.616795   \n",
       "\n",
       "                                  highest_monthly_earnings  \\\n",
       "subscribers                                       0.534388   \n",
       "video views                                       0.637376   \n",
       "uploads                                           0.156062   \n",
       "lowest_monthly_earnings                           0.999937   \n",
       "highest_monthly_earnings                          1.000000   \n",
       "lowest_yearly_earnings                            0.999832   \n",
       "highest_yearly_earnings                           0.999935   \n",
       "video_views_for_the_last_30_days                  0.585788   \n",
       "subscribers_for_last_30_days                      0.617150   \n",
       "\n",
       "                                  lowest_yearly_earnings  \\\n",
       "subscribers                                     0.534883   \n",
       "video views                                     0.638861   \n",
       "uploads                                         0.156639   \n",
       "lowest_monthly_earnings                         0.999914   \n",
       "highest_monthly_earnings                        0.999832   \n",
       "lowest_yearly_earnings                          1.000000   \n",
       "highest_yearly_earnings                         0.999912   \n",
       "video_views_for_the_last_30_days                0.585758   \n",
       "subscribers_for_last_30_days                    0.615874   \n",
       "\n",
       "                                  highest_yearly_earnings  \\\n",
       "subscribers                                      0.534721   \n",
       "video views                                      0.638019   \n",
       "uploads                                          0.155540   \n",
       "lowest_monthly_earnings                          0.999998   \n",
       "highest_monthly_earnings                         0.999935   \n",
       "lowest_yearly_earnings                           0.999912   \n",
       "highest_yearly_earnings                          1.000000   \n",
       "video_views_for_the_last_30_days                 0.585837   \n",
       "subscribers_for_last_30_days                     0.616816   \n",
       "\n",
       "                                  video_views_for_the_last_30_days  \\\n",
       "subscribers                                               0.321182   \n",
       "video views                                               0.360970   \n",
       "uploads                                                   0.079308   \n",
       "lowest_monthly_earnings                                   0.585838   \n",
       "highest_monthly_earnings                                  0.585788   \n",
       "lowest_yearly_earnings                                    0.585758   \n",
       "highest_yearly_earnings                                   0.585837   \n",
       "video_views_for_the_last_30_days                          1.000000   \n",
       "subscribers_for_last_30_days                              0.366320   \n",
       "\n",
       "                                  subscribers_for_last_30_days  \n",
       "subscribers                                           0.361800  \n",
       "video views                                           0.203693  \n",
       "uploads                                               0.009067  \n",
       "lowest_monthly_earnings                               0.616795  \n",
       "highest_monthly_earnings                              0.617150  \n",
       "lowest_yearly_earnings                                0.615874  \n",
       "highest_yearly_earnings                               0.616816  \n",
       "video_views_for_the_last_30_days                      0.366320  \n",
       "subscribers_for_last_30_days                          1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_cols = ['subscribers', 'video views', 'uploads', \n",
    "                  'lowest_monthly_earnings', 'highest_monthly_earnings', \n",
    "                  'lowest_yearly_earnings', 'highest_yearly_earnings',\n",
    "                  'video_views_for_the_last_30_days', 'subscribers_for_last_30_days']\n",
    "\n",
    "correlation_matrix = data[numerical_cols].corr()\n",
    "\n",
    "correlation_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code calculates and visualizes the trend in YouTube channel creation over the years using the \"created_year\" column. Once you've completed this step, you can proceed to the next analysis. If you'd like to continue with the next step or have any questions, please feel free to ask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "yearly_trend = data['created_year'].value_counts().sort_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the YouTube channel creation trend over the years\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(yearly_trend.index, yearly_trend.values, marker='o', linestyle='-')\n",
    "plt.title('YouTube Channel Creation Trend Over the Years')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Channels')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code calculates and visualizes the number of YouTube channels in each category using a bar plot. It will help you understand which categories have the most channels. If you have any questions or would like to proceed with the next analysis step, please feel free to ask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the number of channels in each category\n",
    "\n",
    "category_counts = data['category'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_counts = category_counts.sort_values(ascending=False)\n",
    "# Sort the categories by the number of channels in descending order\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the number of channels in each category using a bar plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.barplot(x=category_counts.values, y=category_counts.index, palette=\"viridis\")\n",
    "plt.title('Number of YouTube Channels in Each Category')\n",
    "plt.xlabel('Number of Channels')\n",
    "plt.ylabel('Category')\n",
    "plt.grid(axis='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This code calculates and visualizes the total number of YouTube channels from each country using a bar plot. It will provide insights into which countries have the highest number of YouTube channels. If you have any questions or would like to proceed with the next analysis step, please feel free to ask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the total number of channels from each country\n",
    "country_counts = data['Country'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort the countries by the total number of channels in descending order\n",
    "country_counts = country_counts.sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the total number of channels from each country using a bar plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.barplot(x=country_counts.values, y=country_counts.index, palette=\"coolwarm\")\n",
    "plt.title('Total Number of YouTube Channels by Country')\n",
    "plt.xlabel('Number of Channels')\n",
    "plt.ylabel('Country')\n",
    "plt.grid(axis='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot to analyze the relationship between \"Gross tertiary education enrollment (%)\" and \"Unemployment rate\"\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.scatterplot(x='Gross tertiary education enrollment (%)', y='Unemployment rate', data=data)\n",
    "plt.title('Scatter Plot: Education Enrollment vs. Unemployment Rate')\n",
    "plt.xlabel('Gross tertiary education enrollment (%)')\n",
    "plt.ylabel('Unemployment rate')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis 2: Correlation Matrix Heatmap\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select numerical columns for correlation analysis\n",
    "numerical_cols = ['subscribers', 'video views', 'uploads', \n",
    "                  'lowest_monthly_earnings', 'highest_monthly_earnings', \n",
    "                  'lowest_yearly_earnings', 'highest_yearly_earnings',\n",
    "                  'video_views_for_the_last_30_days', 'subscribers_for_last_30_days']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the correlation matrix\n",
    "correlation_matrix = data[numerical_cols].corr()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a heatmap to visualize the correlation matrix\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\", linewidths=.5)\n",
    "plt.title('Correlation Matrix Heatmap')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explanation: We create a heatmap to visualize the correlation matrix, with annotations to show the correlation coefficients. This heatmap provides insights into how strongly each numerical variable is related to others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regression Models:\n",
    "\n",
    "Linear Regression: To predict the number of subscribers for YouTube channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select features and target variable\n",
    "X = data[['video views', 'uploads']]\n",
    "y = data['subscribers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and train a Linear Regression model\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the test set\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 107379845764229.67\n",
      "R-squared: 0.8670140324743885\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "print(\"R-squared:\", r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize Linear Regression predictions\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_pred, alpha=0.5)\n",
    "plt.title('Linear Regression: True vs. Predicted Subscribers')\n",
    "plt.xlabel('True Subscribers')\n",
    "plt.ylabel('Predicted Subscribers')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Non-Linear Regression (e.g., Ridge, Lasso, ElasticNet): To handle complex relationships in predicting subscriber counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "from sklearn.linear_model import Ridge, Lasso, ElasticNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#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>Ridge()</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\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Ridge()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and train Ridge Regression model\n",
    "ridge_model = Ridge(alpha=1.0)\n",
    "ridge_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#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>Lasso()</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\">Lasso</label><div class=\"sk-toggleable__content\"><pre>Lasso()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Lasso()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and train Lasso Regression model\n",
    "lasso_model = Lasso(alpha=1.0)\n",
    "lasso_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ElasticNet()</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ElasticNet</label><div class=\"sk-toggleable__content\"><pre>ElasticNet()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "ElasticNet()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and train ElasticNet Regression model\n",
    "elasticnet_model = ElasticNet(alpha=1.0, l1_ratio=0.5)\n",
    "elasticnet_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions using each model\n",
    "ridge_predictions = ridge_model.predict(X_test)\n",
    "lasso_predictions = lasso_model.predict(X_test)\n",
    "elasticnet_predictions = elasticnet_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge Mean Squared Error: 107379845764229.9\n",
      "Lasso Mean Squared Error: 107379845764235.2\n",
      "ElasticNet Mean Squared Error: 107379845764289.69\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Evaluate the models\n",
    "ridge_mse = mean_squared_error(y_test, ridge_predictions)\n",
    "lasso_mse = mean_squared_error(y_test, lasso_predictions)\n",
    "elasticnet_mse = mean_squared_error(y_test, elasticnet_predictions)\n",
    "\n",
    "print(\"Ridge Mean Squared Error:\", ridge_mse)\n",
    "print(\"Lasso Mean Squared Error:\", lasso_mse)\n",
    "print(\"ElasticNet Mean Squared Error:\", elasticnet_mse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Classification Models:\n",
    "\n",
    "Decision Trees or Random Forests: To classify channels as successful or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# Load the dataset and preprocess if needed\n",
    "# Select features and target variable\n",
    "# Split the data into training and testing sets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 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-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 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-5 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-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 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-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 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-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 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-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</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-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=42)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and train a Decision Tree Classifier\n",
    "decision_tree_model = DecisionTreeClassifier(random_state=42)\n",
    "decision_tree_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 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-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 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-6 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-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 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-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 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-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 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-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</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-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and train a Random Forest Classifier\n",
    "random_forest_model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "random_forest_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Make predictions on the test set\n",
    "decision_tree_predictions = decision_tree_model.predict(X_test)\n",
    "random_forest_predictions = random_forest_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Accuracy: 0.009009009009009009\n",
      "Random Forest Accuracy: 0.009009009009009009\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the models\n",
    "decision_tree_accuracy = accuracy_score(y_test, decision_tree_predictions)\n",
    "random_forest_accuracy = accuracy_score(y_test, random_forest_predictions)\n",
    "\n",
    "print(\"Decision Tree Accuracy:\", decision_tree_accuracy)\n",
    "print(\"Random Forest Accuracy:\", random_forest_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize feature importances from Random Forest Classifier\n",
    "importances = random_forest_model.feature_importances_\n",
    "features = X.columns\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=importances, y=features, palette=\"viridis\")\n",
    "plt.title('Feature Importances (Random Forest)')\n",
    "plt.xlabel('Importance')\n",
    "plt.ylabel('Feature')\n",
    "plt.grid(axis='x')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression: To predict the channel type (individual or brand).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Load the dataset and preprocess if needed\n",
    "# Select features and target variable\n",
    "# Split the data into training and testing sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 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-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 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-7 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-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 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-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 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-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 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-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and train a Logistic Regression model\n",
    "logistic_regression_model = LogisticRegression()\n",
    "logistic_regression_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make predictions on the test set\n",
    "logistic_regression_predictions = logistic_regression_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy: 0.0\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "logistic_regression_accuracy = accuracy_score(y_test, logistic_regression_predictions)\n",
    "\n",
    "print(\"Logistic Regression Accuracy:\", logistic_regression_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the confusion matrix for Logistic Regression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "conf_matrix = confusion_matrix(y_test, logistic_regression_predictions)\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "plt.title('Confusion Matrix (Logistic Regression)')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sentiment Analysis on 'Title' Column:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('-', 26), ('kids', 19), ('tv', 16), ('music', 14), ('official', 12), ('&', 11), ('news', 11), ('rhymes', 9), ('songs', 9), ('ýýýýýýýý', 9)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\pnrde\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# Import the necessary libraries\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from collections import Counter\n",
    "\n",
    "# Download the stop words list\n",
    "nltk.download('stopwords')\n",
    "\n",
    "# Get the titles as text data (assuming your column is named 'Title')\n",
    "text_data = data['Title']\n",
    "\n",
    "# Load the stop words list\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "# Clean and lowercase the text, remove punctuation\n",
    "cleaned_text = text_data.str.lower().str.replace('[^\\w\\s]', '').str.split()\n",
    "\n",
    "# Remove stop words from the cleaned text\n",
    "filtered_text = [[word for word in doc if word not in stop_words] for doc in cleaned_text]\n",
    "\n",
    "# Calculate term frequencies\n",
    "flat_list = [item for sublist in filtered_text for item in sublist]\n",
    "term_frequencies = Counter(flat_list)\n",
    "\n",
    "# Show the most common terms and their frequencies\n",
    "most_common_terms = term_frequencies.most_common(10)\n",
    "print(most_common_terms)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>rank</th>\n",
       "      <th>Youtuber</th>\n",
       "      <th>subscribers</th>\n",
       "      <th>video views</th>\n",
       "      <th>category</th>\n",
       "      <th>Title</th>\n",
       "      <th>uploads</th>\n",
       "      <th>Country</th>\n",
       "      <th>channel_type</th>\n",
       "      <th>video_views_rank</th>\n",
       "      <th>...</th>\n",
       "      <th>lowest_yearly_earnings</th>\n",
       "      <th>highest_yearly_earnings</th>\n",
       "      <th>subscribers_for_last_30_days</th>\n",
       "      <th>created_year</th>\n",
       "      <th>Gross tertiary education enrollment (%)</th>\n",
       "      <th>Population</th>\n",
       "      <th>Unemployment rate</th>\n",
       "      <th>Urban_population</th>\n",
       "      <th>Title_Sentiment</th>\n",
       "      <th>Title_Sentiments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>T-Series</td>\n",
       "      <td>245000000</td>\n",
       "      <td>2.280000e+11</td>\n",
       "      <td>Music</td>\n",
       "      <td>T-Series</td>\n",
       "      <td>20082</td>\n",
       "      <td>India</td>\n",
       "      <td>Music</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6800000.0</td>\n",
       "      <td>108400000.0</td>\n",
       "      <td>2000000.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.366418e+09</td>\n",
       "      <td>5.36</td>\n",
       "      <td>471031528.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>MrBeast</td>\n",
       "      <td>166000000</td>\n",
       "      <td>2.836884e+10</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>MrBeast</td>\n",
       "      <td>741</td>\n",
       "      <td>United States</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>48.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4000000.0</td>\n",
       "      <td>64700000.0</td>\n",
       "      <td>8000000.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>88.2</td>\n",
       "      <td>3.282395e+08</td>\n",
       "      <td>14.70</td>\n",
       "      <td>270663028.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Cocomelon - Nursery Rhymes</td>\n",
       "      <td>162000000</td>\n",
       "      <td>1.640000e+11</td>\n",
       "      <td>Education</td>\n",
       "      <td>Cocomelon - Nursery Rhymes</td>\n",
       "      <td>966</td>\n",
       "      <td>United States</td>\n",
       "      <td>Education</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5900000.0</td>\n",
       "      <td>94800000.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>88.2</td>\n",
       "      <td>3.282395e+08</td>\n",
       "      <td>14.70</td>\n",
       "      <td>270663028.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>SET India</td>\n",
       "      <td>159000000</td>\n",
       "      <td>1.480000e+11</td>\n",
       "      <td>Shows</td>\n",
       "      <td>SET India</td>\n",
       "      <td>116536</td>\n",
       "      <td>India</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5500000.0</td>\n",
       "      <td>87500000.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.366418e+09</td>\n",
       "      <td>5.36</td>\n",
       "      <td>471031528.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Like Nastya</td>\n",
       "      <td>106000000</td>\n",
       "      <td>9.047906e+10</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>Like Nastya Vlog</td>\n",
       "      <td>493</td>\n",
       "      <td>Russia</td>\n",
       "      <td>People</td>\n",
       "      <td>630.0</td>\n",
       "      <td>...</td>\n",
       "      <td>146800.0</td>\n",
       "      <td>2300000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>81.9</td>\n",
       "      <td>1.443735e+08</td>\n",
       "      <td>4.59</td>\n",
       "      <td>107683889.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   rank                    Youtuber  subscribers   video views  \\\n",
       "0     1                    T-Series    245000000  2.280000e+11   \n",
       "2     3                     MrBeast    166000000  2.836884e+10   \n",
       "3     4  Cocomelon - Nursery Rhymes    162000000  1.640000e+11   \n",
       "4     5                   SET India    159000000  1.480000e+11   \n",
       "8     9                 Like Nastya    106000000  9.047906e+10   \n",
       "\n",
       "         category                       Title  uploads        Country  \\\n",
       "0           Music                    T-Series    20082          India   \n",
       "2   Entertainment                     MrBeast      741  United States   \n",
       "3       Education  Cocomelon - Nursery Rhymes      966  United States   \n",
       "4           Shows                   SET India   116536          India   \n",
       "8  People & Blogs            Like Nastya Vlog      493         Russia   \n",
       "\n",
       "    channel_type  video_views_rank  ...  lowest_yearly_earnings  \\\n",
       "0          Music               1.0  ...               6800000.0   \n",
       "2  Entertainment              48.0  ...               4000000.0   \n",
       "3      Education               2.0  ...               5900000.0   \n",
       "4  Entertainment               3.0  ...               5500000.0   \n",
       "8         People             630.0  ...                146800.0   \n",
       "\n",
       "   highest_yearly_earnings  subscribers_for_last_30_days  created_year  \\\n",
       "0              108400000.0                     2000000.0        2006.0   \n",
       "2               64700000.0                     8000000.0        2012.0   \n",
       "3               94800000.0                     1000000.0        2006.0   \n",
       "4               87500000.0                     1000000.0        2006.0   \n",
       "8                2300000.0                      100000.0        2016.0   \n",
       "\n",
       "   Gross tertiary education enrollment (%)    Population  Unemployment rate  \\\n",
       "0                                     28.1  1.366418e+09               5.36   \n",
       "2                                     88.2  3.282395e+08              14.70   \n",
       "3                                     88.2  3.282395e+08              14.70   \n",
       "4                                     28.1  1.366418e+09               5.36   \n",
       "8                                     81.9  1.443735e+08               4.59   \n",
       "\n",
       "   Urban_population  Title_Sentiment  Title_Sentiments  \n",
       "0       471031528.0              0.0               0.0  \n",
       "2       270663028.0              0.0               0.0  \n",
       "3       270663028.0              0.0               0.0  \n",
       "4       471031528.0              0.0               0.0  \n",
       "8       107683889.0              0.0               0.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the necessary libraries\n",
    "from textblob import TextBlob\n",
    "\n",
    "# Get the titles as a list (assuming your column is named 'Title')\n",
    "titles = data['Title']\n",
    "\n",
    "# Perform sentiment analysis for each title\n",
    "sentiments = []\n",
    "for title in titles:\n",
    "    analysis = TextBlob(title)\n",
    "    sentiment = analysis.sentiment.polarity  # Sentiment ranges from -1 (negative) to 1 (positive)\n",
    "    sentiments.append(sentiment)\n",
    "\n",
    "# Add the sentiment scores to your dataframe\n",
    "data['Title_Sentiments'] = sentiments\n",
    "\n",
    "# Examine the results\n",
    "data.head()\n"
   ]
  }
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