agmvc-DataAnalysis-LogisticRegressionModel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ebc171b4",
   "metadata": {},
   "source": [
    "## Social Data Science Project\n",
    "\n",
    "###  <span style=\"color:#044e8a\"> The Impact of Video Length and Interactivity on YouTube Channel Size and Video Popularity:</span> \n",
    "### <span style=\"color:#044e8a\"> Analysis of Greece's Most-Viewed Channels </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b0f7817",
   "metadata": {},
   "source": [
    "<span style=\"color: white; background-color: #044e8a; padding: 2px; border-radius: 3px;\">\n",
    "Objective: </span>\n",
    "\n",
    "To explore how video length, content type, and interactivity affect channel size and video popularity among the most-viewed YouTube channels in Greece using data from the YouTube API.\n",
    "\n",
    "Byun et al. (2023). The effect of YouTube comment interaction on video engagement: focusing on interactivity centralization and creators' interactivity. Available at: https://www.emerald.com/insight/content/doi/10.1108/oir-04-2022-0217/full/html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f6eebf",
   "metadata": {},
   "source": [
    "<span style=\"color: white; background-color: #044e8a; padding: 2px; border-radius: 3px;\">\n",
    "TABLE OF CONTENTS</span>\n",
    "\n",
    "|**No.**| **Chapter** | **Sections** |\n",
    "|:-------:|:-------------------|:-----------------|\n",
    "|**6**| **Data Analysis**|\n",
    "|||**6.1 Word Count & Most popular word**\n",
    "|||**6.2 Linear Regression**\n",
    "|||**6.3 Logistic Regression**\n",
    "|||**6.4 Logistic model include a Control Variable**\n",
    "|||**6.5 Logistic model include an Interaction Term**\n",
    "|||**6.6 Logistic model include a Categorical Predictor**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c61aa4d4",
   "metadata": {},
   "source": [
    "<span style=\"color: white; background-color: #145c02; padding: 2px; border-radius: 3px;\">\n",
    "Libraries used: </span>\n",
    "\n",
    "*this section lists all the libraries required for data collection, cleaning, analysis, and visualization in this project.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8e00756",
   "metadata": {},
   "source": [
    "<span style=\"color:#145c02\"> **Data Collection** </span> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e7a81b82",
   "metadata": {},
   "outputs": [],
   "source": [
    "from googleapiclient.discovery import build #for YouTube API access\n",
    "from bs4 import BeautifulSoup #for HTML parsing during web scraping\n",
    "import requests #for making HTTP requests\n",
    "import urllib.parse #for encoding URLs\n",
    "from fake_useragent import UserAgent #for generating random user agents-in scraping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da49a9cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time #for adding delays between API or scraping requests\n",
    "import random #for using random wait times in scraping to avoid detection\n",
    "from tqdm import tqdm #for progress bars during loops\n",
    "import re #for regular expressions to clean or parse text\n",
    "import json #for handling JSON responses the API"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "add5bd65",
   "metadata": {},
   "source": [
    "<span style=\"color:#145c02\"> **Data Cleaning, Processing, Analysis** </span> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb916440",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd #for working with structured datasets\n",
    "import numpy as np #for numerical computations\n",
    "import datetime  # For handling date/time conversions and computations\n",
    "from isodate import parse_duration #for handling ISO 8601 format commonly used in APIs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d9af5cff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import hashlib #provides secure hashing algorithms like sha256 for anonymizing sensitive data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b296b436",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f5952776",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm #for statistical analysis and regression modeling\n",
    "from statsmodels.formula.api import ols #for building regression models using formulas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4157b170",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.stats.outliers_influence import variance_inflation_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9e47182d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler #for normalizing/standardizing data\n",
    "from sklearn.model_selection import train_test_split #for splitting data into training/testing sets\n",
    "from sklearn.metrics import mean_squared_error, accuracy_score #for model evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4529c127",
   "metadata": {},
   "source": [
    "<span style=\"color:#145c02\">**Visualization**</span> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "da602810",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as plt #core library for visualizations\n",
    "import matplotlib.pyplot as plt #for plotting and customizing figures\n",
    "import seaborn as sns #for statistical data visualizations\n",
    "import plotly.express as px #for interactive and advanced plotes\n",
    "from wordcloud import WordCloud #for generating word clouds,text analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9a087ab",
   "metadata": {},
   "source": [
    "# <span style=\"color:#1d2224\"> **6 | Data Analysis** </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e8b8674",
   "metadata": {},
   "source": [
    "### 6.1 | Word Count & Most popular word "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ef795ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('stopwords-el.txt', 'r') as file:\n",
    "    stop_words = [word.strip().lower() for word in file.readlines()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "09f0d71e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ακομα', 'ακομη', 'ακριβως', 'αληθεια', 'αληθινα', 'αλλα']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stop_words[4:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "785ccb9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "descriptions = ' '.join(video_data['Description_c'].dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d62b26b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ένα ταξίδι στο μέλλον του χρηματοπιστωτικού συστήματος του χρήματος και της οικονομίας πως η τραπεζική καινοτομία θα επηρεάσει την ζωή μας στα επόμενα χρόνια   ευχαριστούμε την freedom24 για την οικον'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptions[:200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "39f5a9c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "descriptions_words = descriptions.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "18dbba69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['της', 'οικονομίας', 'πως', 'η', 'τραπεζική']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptions_words[10:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4e081ab6",
   "metadata": {},
   "outputs": [],
   "source": [
    "descriptions_words = [word for word in descriptions.split() if word not in stop_words]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "542dcdd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['επόμενα', 'χρόνια', 'ευχαριστούμε', 'freedom24', 'οικονομική']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "descriptions_words[10:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "37ffa13b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#function counts the words and then sorts them to count the most used ones.\n",
    "def my_funlist(ls):\n",
    "    word_counts = {}\n",
    "    for word in ls:\n",
    "        if word in word_counts:\n",
    "            word_counts[word] += 1\n",
    "        else:\n",
    "            word_counts[word] = 1\n",
    "    sorted_word_counts = sorted(word_counts.items(), key=lambda item: item[1], reverse=True)\n",
    "    return sorted_word_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "02ae051d",
   "metadata": {},
   "outputs": [],
   "source": [
    "word_counts = my_funlist(descriptions_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e4e03887",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most Common Words:\n",
      "instagram: 8766\n",
      "βίντεο: 5742\n",
      "facebook: 5479\n",
      "κανάλι: 5110\n",
      "subscribe: 4654\n",
      "fifa: 4348\n",
      "and: 4197\n",
      "for: 3844\n",
      "business: 3792\n",
      "εδώ: 3711\n"
     ]
    }
   ],
   "source": [
    "print(\"Most Common Words:\")\n",
    "for word, count in word_counts[:10]:\n",
    "    print(f\"{word}: {count}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7df4de42",
   "metadata": {},
   "source": [
    "*Translation:* \\\n",
    "**Most Common Words:** \\\n",
    "instagram: 8766\\\n",
    "video: 5742\\\n",
    "facebook: 5479\\\n",
    "channel: 5110\\\n",
    "subscribe: 4654\\\n",
    "fifa: 4348\\\n",
    "and: 4197\\\n",
    "for: 3844\\\n",
    "business: 3792\\\n",
    "here: 3711"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1f722a39",
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to plot word cloud\n",
    "def word_cloud_fun(x):\n",
    "    text = ' '.join(x)\n",
    "    wordcloud = WordCloud(width=600, height=200, background_color='white').generate(text)\n",
    "    plt.figure(figsize=(10, 5))\n",
    "    plt.imshow(wordcloud, interpolation='bilinear')\n",
    "    plt.axis('off')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "c15b9f5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "word_cloud_fun(descriptions_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5eb4c045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'instagram'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_counts[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "60b982c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "most_popular_word = word_counts[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "33cc9372",
   "metadata": {},
   "outputs": [],
   "source": [
    "video_data['Most_Popular_Word_Count'] = video_data['Description_c'].apply(\n",
    "    lambda x: x.split().count(most_popular_word) if isinstance(x, str) else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a25f2837",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Most_Popular_Word_Count\n",
       "1    6700\n",
       "0    6223\n",
       "2     561\n",
       "3     250\n",
       "4      39\n",
       "5       5\n",
       "7       1\n",
       "6       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_data['Most_Popular_Word_Count'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3b0b511e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "plt.hist(video_data['Most_Popular_Word_Count'], bins=10, color=\"#72B5BA\", edgecolor=\"black\")\n",
    "plt.title(\"Histogram of Most Popular Word Counts\", fontsize=16, fontweight=\"bold\")\n",
    "plt.xlabel(\"Most Popular Word Count\", fontsize=14)\n",
    "plt.ylabel(\"Frequency\", fontsize=14)\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf56c68a",
   "metadata": {},
   "source": [
    "### 6.2 | Linear Regression: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "1b48af3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(channel_data['Total_Videos'], channel_data['Subscribers'], color='#fc9803')\n",
    "\n",
    "z = np.polyfit(channel_data['Total_Videos'], channel_data['Subscribers'], 1)\n",
    "p = np.poly1d(z)\n",
    "\n",
    "plt.plot(channel_data['Total_Videos'], p(channel_data['Total_Videos']), color='red', label=f\"Trend Line: y={z[0]:.2f}x + {z[1]:.2f}\")\n",
    "plt.xlabel('Total Number of Videos', fontweight='bold')\n",
    "plt.ylabel('Subscribers', fontweight='bold')\n",
    "plt.title('Relationship of Upload Activity and Subscribers', fontweight='bold', pad=20, fontsize=16)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "343b319b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = channel_data['Total_Videos']\n",
    "y = channel_data['Subscribers']\n",
    "X = sm.add_constant(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2302284e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            Subscribers   R-squared:                       0.568\n",
      "Model:                            OLS   Adj. R-squared:                  0.556\n",
      "Method:                 Least Squares   F-statistic:                     46.10\n",
      "Date:                Wed, 25 Dec 2024   Prob (F-statistic):           7.15e-08\n",
      "Time:                        00:31:54   Log-Likelihood:                -508.97\n",
      "No. Observations:                  37   AIC:                             1022.\n",
      "Df Residuals:                      35   BIC:                             1025.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "================================================================================\n",
      "                   coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "const         5003.0289   5.66e+04      0.088      0.930    -1.1e+05     1.2e+05\n",
      "Total_Videos   745.9208    109.862      6.790      0.000     522.890     968.952\n",
      "==============================================================================\n",
      "Omnibus:                       44.523   Durbin-Watson:                   2.057\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              223.692\n",
      "Skew:                           2.607   Prob(JB):                     2.67e-49\n",
      "Kurtosis:                      13.858   Cond. No.                         757.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model = sm.OLS(y, X).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e9090d8",
   "metadata": {},
   "source": [
    "**Interpretation of Regression Coefficients:**\n",
    "\n",
    "1. **Intercept (5003):**\n",
    "   - The intercept represents the estimated value of the dependent variable (`Subscribers`) when total numbers of videos (`Total_Videos`) is zero. Practically, this scenario might not be meaningful since channels without videos are unlikely to have subscribers. However, it provides a baseline value for the model.\n",
    "\n",
    "&NewLine;\n",
    "\n",
    "2. **Total Videos (745.9208):**\n",
    "    - For each additional video uploaded (`Total_Videos`) by a channel, the estimated number of subscribers (`Subscribers`) increases by approximately 745.92, holding all else constant. The p-value=0.000<0.001 shows that this relationship is statistically significant, meaning there is a strong evidence that the total number of videos is positively associated with the number of subsribers.\n",
    "   \n",
    "&NewLine;\n",
    "   \n",
    "**Model Fit $R^2$ (0.568):**\n",
    "    - The $R^2$ value suggests that approximately 56.8% of the variation in Subscribers is explained by the number of (`Total_Videos`). While this is a important proportion, other factors likely contribute to the remaining unexplained variation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "976e1093",
   "metadata": {},
   "source": [
    "The scatter plot above suggests that the relationship between the (`Total_Videos`) and (`Subscribers`) may not remain linear as the number of videos increases due to a lot of variability, especially at higher values. For instance, after a certain point, adding more videos might yield fewer additional subscribers (a plateau effect). So, a squared term for (`Total_Videos`) could be beneficial either by capturing the possible diminishing returns effect or improving the model fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0ae6a53e",
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_data['Total_Videos_Squared'] = channel_data['Total_Videos'] ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "11dac9cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xb = channel_data[['Total_Videos', 'Total_Videos_Squared']]\n",
    "Xb = sm.add_constant(Xb)\n",
    "yb = channel_data['Subscribers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "da5d2722",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:            Subscribers   R-squared:                       0.570\n",
      "Model:                            OLS   Adj. R-squared:                  0.545\n",
      "Method:                 Least Squares   F-statistic:                     22.55\n",
      "Date:                Wed, 25 Dec 2024   Prob (F-statistic):           5.83e-07\n",
      "Time:                        00:32:11   Log-Likelihood:                -508.90\n",
      "No. Observations:                  37   AIC:                             1024.\n",
      "Df Residuals:                      34   BIC:                             1029.\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "========================================================================================\n",
      "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------\n",
      "const                 2.384e+04   7.64e+04      0.312      0.757   -1.31e+05    1.79e+05\n",
      "Total_Videos           630.6453    328.149      1.922      0.063     -36.234    1297.525\n",
      "Total_Videos_Squared     0.0930      0.249      0.373      0.711      -0.413       0.599\n",
      "==============================================================================\n",
      "Omnibus:                       46.260   Durbin-Watson:                   2.063\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              243.698\n",
      "Skew:                           2.725   Prob(JB):                     1.21e-53\n",
      "Kurtosis:                      14.330   Cond. No.                     1.04e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.04e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "modelb = sm.OLS(yb, Xb).fit()\n",
    "print(modelb.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbc58612",
   "metadata": {},
   "source": [
    "The squared term (`Total_Videos_Squared`) does not significantly improve the model (p-value=0.711>0.001) and contributes very little to explaining the variation in (`Subscribers`). Also, the improvement in $R^2$ is insignificant compared to the model with only the linear term."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1dddf48",
   "metadata": {},
   "source": [
    "### 6.3 | Logistic Regression: "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d6351d4",
   "metadata": {},
   "source": [
    "A new binary variable was created to classify whether a video is popular or not, based on the median of all views. The median was chosen instead of the mean because it is not affected by outliers, and YouTube view counts often follow a highly skewed distribution, as demonstrated in the visualization above. This skewness is due to a small number of videos achieving extremely high view counts (viral content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "073279ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "median_views = video_data['Views'].median()\n",
    "video_data['Popular'] = (video_data['Views'] > median_views).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8ba61ea6",
   "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>Comments</th>\n",
       "      <th>Popular</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13780.000000</td>\n",
       "      <td>13780.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>507.206676</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2641.939389</td>\n",
       "      <td>0.500018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>162.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>425.250000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>161427.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Comments       Popular\n",
       "count   13780.000000  13780.000000\n",
       "mean      507.206676      0.500000\n",
       "std      2641.939389      0.500018\n",
       "min         0.000000      0.000000\n",
       "25%        50.000000      0.000000\n",
       "50%       162.000000      0.500000\n",
       "75%       425.250000      1.000000\n",
       "max    161427.000000      1.000000"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_data[['Comments', 'Popular']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5a331b53",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = video_data['Comments']\n",
    "y1 = video_data['Popular']\n",
    "X1 = sm.add_constant(X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "68f85ab1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.681404\n",
      "         Iterations 7\n"
     ]
    }
   ],
   "source": [
    "logit_model1 = sm.Logit(y1, X1).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "31fd3a02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                Popular   No. Observations:                13780\n",
      "Model:                          Logit   Df Residuals:                    13778\n",
      "Method:                           MLE   Df Model:                            1\n",
      "Date:                Wed, 25 Dec 2024   Pseudo R-squ.:                 0.01694\n",
      "Time:                        00:32:45   Log-Likelihood:                -9389.7\n",
      "converged:                       True   LL-Null:                       -9551.6\n",
      "Covariance Type:            nonrobust   LLR p-value:                 2.323e-72\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.1657      0.020     -8.176      0.000      -0.205      -0.126\n",
      "Comments       0.0004    3.2e-05     13.523      0.000       0.000       0.000\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "print(logit_model1.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b3afb703",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_odds_const1 = logit_model1.params['const']\n",
    "log_odds_comments1 = logit_model1.params['Comments']\n",
    "\n",
    "odds_const1 = np.exp(log_odds_const1)\n",
    "odds_comments1 = np.exp(log_odds_comments1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "54da9f99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept Odds: 0.8472938994144801\n",
      "Comments Odds: 1.0004334873535667\n"
     ]
    }
   ],
   "source": [
    "print(f\"Intercept Odds: {odds_const1}\")\n",
    "print(f\"Comments Odds: {odds_comments1}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "6fbce111",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-15.27"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentage_const1 = round((odds_const1 - 1) * 100,2)\n",
    "percentage_const1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f3da3ad5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Each additional comment increases likelihood of popularity by: 0.04%\n"
     ]
    }
   ],
   "source": [
    "percentage_more_likely_comments1 = (odds_comments1 - 1) * 100\n",
    "print(f\"Each additional comment increases likelihood of popularity by: {percentage_more_likely_comments1:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26017f55",
   "metadata": {},
   "source": [
    "**Interpretation of Regression Coefficients:**\n",
    "\n",
    "1. **Intercept (-0.1657):**\n",
    "   - The intercept represents the estimated value of the dependent variable (`Popular`) when total numbers of comments (`Comments`) is zero. In this case, a negative coefficient of -0.1657 means that the log-odds of a video with no comments being popular are negative, indicating that videos with no comments are approximately 15.3% less likely to be popular.The p-value=0.000<0.001 shows that this relationship is statistically significant,meaning that the baseline log-odds of a video being popular when there are zero comments is significantly different from zero.\n",
    "\n",
    "&NewLine;\n",
    "\n",
    "2. **Comments (0.0004):**\n",
    "    - For every additional video comment (`Comments`), the log-odds of a video being classified as popular (`Popular`) increases by  0.0004. In this case the likelihood of a video being popular increases by approximately 0.04%. The p-value=0.000<0.001 shows that this relationship is statistically significant, meaning that there is a strong relationship between the number of comments and the likelihood of a video being popular. \n",
    "   \n",
    "&NewLine;\n",
    "   \n",
    "**Pseudo $R^2$ (0.01694):**\n",
    "    - The $R^2$ that the model explains about 1.69% of the variance in popularity. Of course, other factors likely play a larger role in determining popularity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f89ed002",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "video_data['Predicted_Probability'] = logit_model1.predict(sm.add_constant(video_data['Comments']))\n",
    "\n",
    "plt.scatter(video_data['Comments'], video_data['Predicted_Probability'], alpha=0.5)\n",
    "plt.xlabel('Number of Comments')\n",
    "plt.ylabel('Predicted Probability of Popular')\n",
    "plt.title('Relationship Between Comments and Popularity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d9ae88b",
   "metadata": {},
   "source": [
    "The large range of Comments (from very low to 160,000) could benefit from a log transformation to compress the scale and better capture the diminishing returns at higher comment counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "173912a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "video_data['Log_Comments'] = np.log1p(video_data['Comments'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4337798a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.566724\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "X2 = video_data['Log_Comments']\n",
    "X2 = sm.add_constant(X2)\n",
    "logit_model_log2 = sm.Logit(video_data['Popular'], X2).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "ae5cc99d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                Popular   No. Observations:                13780\n",
      "Model:                          Logit   Df Residuals:                    13778\n",
      "Method:                           MLE   Df Model:                            1\n",
      "Date:                Wed, 25 Dec 2024   Pseudo R-squ.:                  0.1824\n",
      "Time:                        00:33:20   Log-Likelihood:                -7809.5\n",
      "converged:                       True   LL-Null:                       -9551.6\n",
      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
      "================================================================================\n",
      "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "const           -3.5940      0.080    -45.130      0.000      -3.750      -3.438\n",
      "Log_Comments     0.7258      0.015     47.490      0.000       0.696       0.756\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "print(logit_model_log2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "05c0a25f",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_odds_const2 = logit_model_log2.params['const']\n",
    "log_odds_comments2 = logit_model_log2.params['Log_Comments']\n",
    "\n",
    "odds_const2 = np.exp(log_odds_const2)\n",
    "odds_comments2 = np.exp(log_odds_comments2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "1c131bca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept Odds: 0.02748843103214267\n",
      "Comments Odds: 2.066339193933272\n"
     ]
    }
   ],
   "source": [
    "print(f\"Intercept Odds: {odds_const2}\")\n",
    "print(f\"Comments Odds: {odds_comments2}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c083217f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-97.25"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentage_const2 = round((odds_const2 - 1) * 100,2)\n",
    "percentage_const2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1904f240",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Each additional comment increases likelihood of popularity by: 106.63%\n"
     ]
    }
   ],
   "source": [
    "percentage_more_likely_comments2 = (odds_comments2 - 1) * 100\n",
    "print(f\"Each additional comment increases likelihood of popularity by: {percentage_more_likely_comments2:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbb74c41",
   "metadata": {},
   "source": [
    "**Interpretation of Regression Coefficients:**\n",
    "\n",
    "1. **Intercept (-3.5940):**\n",
    "   - The intercept represents the estimated log-odds of a video being classified as popular (`Popular`) when the log-transformed numbers of comments (`Log_Comments`) is zero, which corresponds to a video with one comment. In this case, a negative coefficient of -3.5940 indicates that the log-odds of a video with only one comment being popular are negative, indicating that videos with no comments are negative. This translates to an odds ratio of approximately 0.0275, meaning that videos with one comment are about 97.25% less likely to be calssified as popular. The p-value=0.000<0.001 shows that this relationship is statistically significant,meaning that the baseline log-odds of a video being popular when comments are significantly different from zero.\n",
    "\n",
    "&NewLine;\n",
    "\n",
    "2. **Log Comments (0.7258):**\n",
    "    - For every additional unit increase in the log-transformed number of comments (`Log_Comments`), the log-odds of a video being classified as popular (`Popular`) increases by 0.7258. This translates to an odds ratio of 2.066, indicating that the likelihood of a video being popular approximately doubles (increases by 106.63%) for every unit increase in (`Log_Comments`). The p-value=0.000<0.001 confirms that this relationship is statistically significant, indicating a strong and meaningful relationship between the number of comments and the likelihood of a video being popular\n",
    "   \n",
    "&NewLine;\n",
    "   \n",
    "**Pseudo $R^2$ (0.1824):**\n",
    "    - The $R^2$ that the model explains about 18.24% of the variance in popularity. Of course, other factors likely play a larger role in determining popularity."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f367f6a3",
   "metadata": {},
   "source": [
    "### 6.4 | Logistic model include a Control Variable: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "70423654",
   "metadata": {},
   "outputs": [],
   "source": [
    "X3 = video_data[['Log_Comments', 'Published_Year']]\n",
    "y3 = video_data['Popular']\n",
    "X3 = sm.add_constant(X3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3fc2931d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.502869\n",
      "         Iterations 8\n"
     ]
    }
   ],
   "source": [
    "logit_model3 = sm.Logit(y3, X3).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "9feadc6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                Popular   No. Observations:                13780\n",
      "Model:                          Logit   Df Residuals:                    13777\n",
      "Method:                           MLE   Df Model:                            2\n",
      "Date:                Wed, 25 Dec 2024   Pseudo R-squ.:                  0.2745\n",
      "Time:                        00:33:36   Log-Likelihood:                -6929.5\n",
      "converged:                       True   LL-Null:                       -9551.6\n",
      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
      "==================================================================================\n",
      "                     coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "const           -677.4722     18.335    -36.949      0.000    -713.409    -641.536\n",
      "Log_Comments       0.9584      0.019     50.914      0.000       0.922       0.995\n",
      "Published_Year     0.3328      0.009     36.780      0.000       0.315       0.351\n",
      "==================================================================================\n"
     ]
    }
   ],
   "source": [
    "print(logit_model3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "356753cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_odds_const3 = logit_model3.params['const']\n",
    "log_odds_comments3 = logit_model3.params['Log_Comments']\n",
    "log_odds_year3 = logit_model3.params['Published_Year']\n",
    "\n",
    "odds_const3 = np.exp(log_odds_const3)\n",
    "odds_comments3 = np.exp(log_odds_comments3)\n",
    "odds_year3 = np.exp(log_odds_year3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "b0de6c75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept Odds: 0.0000\n",
      "Comments Odds: 2.6076\n",
      " Year Odds: 1.3949\n"
     ]
    }
   ],
   "source": [
    "print(f\"Intercept Odds: {odds_const3:.4f}\")\n",
    "print(f\"Comments Odds: {odds_comments3:.4f}\")\n",
    "print(f\" Year Odds: {odds_year3:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2ffdbed0",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentage_const3 = round((odds_const3 - 1) * 100, 2)\n",
    "percentage_comments3 = round((odds_comments3 - 1) * 100, 2)\n",
    "percentage_year3 = round((odds_year3 - 1) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "edf69ad7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept Percentage Change: -100.0\n",
      "Comments Percentage Change: 160.76\n",
      " Year Percentage Change: 39.49\n"
     ]
    }
   ],
   "source": [
    "print(f\"Intercept Percentage Change: {percentage_const3}\")\n",
    "print(f\"Comments Percentage Change: {percentage_comments3}\")\n",
    "print(f\" Year Percentage Change: {percentage_year3}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4660bf15",
   "metadata": {},
   "source": [
    "**Interpretation of Regression Coefficients:**\n",
    "\n",
    "1. **Intercept (-677.4722):**\n",
    "   - The intercept represents the estimated log-odds of a video being classified as popular (`Popular`) when the log-transformed numbers of comments (`Log_Comments`) and the published year (`Published_Year`) is zero. Practically, this is impossible,but the large negative value indicates extremely low odds of popularity.A percentage change of -100.0% signifies that videos in this hypothetical baseline scenario are completely unlikely to be popular. The p-value=0.000<0.001 shows that baseline log-odds is statistically significant.\n",
    "\n",
    "&NewLine;\n",
    "\n",
    "2. **Log Comments (0.9584):**\n",
    "    - For every additional unit increase in the log-transformed number of comments (`Log_Comments`), the log-odds of a video being classified as popular (`Popular`) increases by 0.9584. This translates to an odds ratio of 2.6076, indicating that for every unit increase the likelihood of a video being popular increase by approximately 2.61 times.A percentage change of 160.76%, means that videos with higher numbers of comments are significantly more likely to be popular.The p-value=0.000<0.001 confirms that this relationship is statistically significant, indicating a strong and positive relationship between the number of comments and the likelihood of a video being popular\n",
    "    \n",
    "3. **Published_Year (0.3328):**\n",
    "    - For every additional year (`Published_Year`) the log-odds of a video being classified as popular (`Popular`) increases by 0.3328. This translates to an odds ratio of 1.3949, meaining that the odds of a video being popular increase by approximately 1.39 times for each passing year. A percentage change of 39.49%, means a consistent trend of increasing video popularity over time.The p-value=0.000<0.001 confirms that this relationship is statistically significant, highlighting a meaningful temporal trend in video popularity.\n",
    "   \n",
    "&NewLine;\n",
    "   \n",
    "**Pseudo $R^2$ (0.2745):**\n",
    "    - The $R^2$ that the model explains about 27.45% of the variance in popularity. Of course, other factors likely play a larger role in determining popularity."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69e13e3a",
   "metadata": {},
   "source": [
    "The inclusion of the control variable `Published_Year` in the model helps account for temporal trends that might influence the popularity of videos over time. Specifically, it indicates that videos published in more recent years are more likely to be popular, as well as improves the model's ability to explain the variance in the dependent variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c7de15b",
   "metadata": {},
   "source": [
    "### 6.5 | Logistic model include an Interaction Term: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0856f165",
   "metadata": {},
   "outputs": [],
   "source": [
    "vidcom_data = video_data.merge(\n",
    "    comments_data[['Video_ID', 'Replies_Presence']],\n",
    "    left_on='Id',\n",
    "    right_on='Video_ID',\n",
    "    how='left'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "c32d0d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "vidcom_data['Replies_Presence'] = vidcom_data['Replies_Presence'].fillna(0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a4767711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vidcom_data['Replies_Presence'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3bfaca63",
   "metadata": {},
   "outputs": [],
   "source": [
    "vidcom_data['Interaction_Term'] = vidcom_data['Replies_Presence'] * vidcom_data['Log_Comments']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d8455e7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X4 = vidcom_data[['Log_Comments', 'Published_Year','Interaction_Term']]\n",
    "y4 = vidcom_data['Popular']\n",
    "X4 = sm.add_constant(X4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "da75ab35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.672219\n",
      "         Iterations 5\n"
     ]
    }
   ],
   "source": [
    "logit_model4 = sm.Logit(y4, X4).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "f0559033",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                Popular   No. Observations:                44803\n",
      "Model:                          Logit   Df Residuals:                    44799\n",
      "Method:                           MLE   Df Model:                            3\n",
      "Date:                Wed, 25 Dec 2024   Pseudo R-squ.:                 0.01185\n",
      "Time:                        00:33:57   Log-Likelihood:                -30117.\n",
      "converged:                       True   LL-Null:                       -30479.\n",
      "Covariance Type:            nonrobust   LLR p-value:                2.710e-156\n",
      "====================================================================================\n",
      "                       coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "const             -148.7897      8.304    -17.918      0.000    -165.065    -132.514\n",
      "Log_Comments         0.0026      0.005      0.496      0.620      -0.008       0.013\n",
      "Published_Year       0.0737      0.004     17.947      0.000       0.066       0.082\n",
      "Interaction_Term     0.1484      0.008     18.639      0.000       0.133       0.164\n",
      "====================================================================================\n"
     ]
    }
   ],
   "source": [
    "print(logit_model4.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "73f653e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_odds_const4 = logit_model4.params['const']\n",
    "log_odds_comments4 = logit_model4.params['Log_Comments']\n",
    "log_odds_year4 = logit_model4.params['Published_Year']\n",
    "log_odds_int4 = logit_model4.params['Interaction_Term']\n",
    "\n",
    "odds_const4 = np.exp(log_odds_const4)\n",
    "odds_comments4 = np.exp(log_odds_comments4)\n",
    "odds_year4 = np.exp(log_odds_year4)\n",
    "odds_int4 = np.exp(log_odds_int4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "f5838012",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept Odds: 0.0000\n",
      "Comments Odds: 1.0027\n",
      "Year Odds: 1.0765\n",
      "Interaction Term Odds: 1.1600\n"
     ]
    }
   ],
   "source": [
    "print(f\"Intercept Odds: {odds_const4:.4f}\")\n",
    "print(f\"Comments Odds: {odds_comments4:.4f}\")\n",
    "print(f\"Year Odds: {odds_year4:.4f}\")\n",
    "print(f\"Interaction Term Odds: {odds_int4:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "8d669b73",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentage_const4 = round((odds_const4 - 1) * 100, 2)\n",
    "percentage_comments4 = round((odds_comments4 - 1) * 100, 2)\n",
    "percentage_year4 = round((odds_year4 - 1) * 100, 2)\n",
    "percentage_int4 = round((odds_int4 - 1) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "66eb55e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept Percentage Change: -100.0\n",
      "Comments Percentage Change: 0.27\n",
      "Year Percentage Change: 7.65\n",
      "Interaction Percentage Change: 16.0\n"
     ]
    }
   ],
   "source": [
    "print(f\"Intercept Percentage Change: {percentage_const4}\")\n",
    "print(f\"Comments Percentage Change: {percentage_comments4}\")\n",
    "print(f\"Year Percentage Change: {percentage_year4}\")\n",
    "print(f\"Interaction Percentage Change: {percentage_int4}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17cb9aa1",
   "metadata": {},
   "source": [
    "**Interpretation of Regression Coefficients:**\n",
    "\n",
    "1. **Intercept (-148.7897):**\n",
    "   - The intercept represents the baseline log-odds of a video being classified as popular (`Popular`) when all predictors are at their baseline values (Log_Comments = 0, Published_Year = 0, and Interaction_Term = 0). The odds of popularity at this baseline are essentially 0, indicating that videos with no comments, from year zero, and no replies presence are impossible to be popular. The p-value=0.000<0.001 shows that baseline log-odds is statistically significant.\n",
    "   \n",
    "&NewLine;\n",
    "\n",
    "2. **Log Comments (0.0026):**\n",
    "    - For every additional unit increase in the log-transformed number of comments (`Log_Comments`), the log-odds of a video being classified as popular (`Popular`) increases by 0.0026. This translates to an odds ratio of 1.0027,, indicating that for every unit increase the likelihood of a video being popular increase by approximately 1.0027 times. The p-value=0.620>0.001 indicates that this relationship is **NOT** statistically significant,when controlling `Published_Year` and the `Interaction Term`.\n",
    "    \n",
    "3. **Published_Year (0.0737):**\n",
    "    - For every additional year (`Published_Year`) the log-odds of a video being classified as popular (`Popular`) increases by 0.0737 (or percentage Change: 0.27%). This translates to an odds ratio of 1.0765, meaining that the odds of a video being popular increase by approximately 1.076 times for each passing year. The p-value=0.000<0.001 confirms that this relationship is statistically significant, highlighting a meaningful temporal trend in video popularity or may reflect changes in audience preferences, platform algorithms, or other temporal factors.\n",
    "    \n",
    "4. **Interaction Term (Log Comments x Replies Presence (0.1484):**\n",
    "    - The interaction between `Log Comments` and  `Replies Presence` has a strong positive effect on popularity (`Popular`). For every one unit increase in the interaction term, the odds of a video being popular increase by 0.1484 (or percentage Change: 16.0%). The p-value=0.000<0.001 confirms that this interaction is statistically,  significant,and highlights that videos with higher comments are significantly more likely to be popular when there replying comments too (thread-conversation).\n",
    "   \n",
    "&NewLine;\n",
    "   \n",
    "**Pseudo $R^2$ (0.01185):**\n",
    "    - The $R^2$ that the model explains about 1.2%% of the variance in popularity.This is a small effect, suggesting that while the included predictors contribute meaningfully, other factors not captured in the model likely play a substantial role in determining video popularity."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e4c02b2",
   "metadata": {},
   "source": [
    "The model with the interaction term explains far less variance, suggesting that while replies add context, other factors likely drive video popularity. However, the intercept is less extreme suggesting that the interaction term helps capture some baseline variance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba3908bc",
   "metadata": {},
   "source": [
    "### 6.6 | Logistic model include a Categorical Predictor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b352e712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['long', 'medium', nan, 'short', 'super long'], dtype=object)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vidcom_data['Video_Length_Category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "36e22690",
   "metadata": {},
   "outputs": [],
   "source": [
    "vidcom_data['Video_Length_Category'] = vidcom_data['Video_Length_Category'].fillna('unknown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ff27d053",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/qt/nkv93n510wlcddjjxc58klyr0000gn/T/ipykernel_4780/2817792131.py:1: DeprecationWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, pd.CategoricalDtype) instead\n",
      "  print(pd.api.types.is_categorical_dtype(vidcom_data['Video_Length_Category']))\n"
     ]
    }
   ],
   "source": [
    "print(pd.api.types.is_categorical_dtype(vidcom_data['Video_Length_Category']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8d3cc77f",
   "metadata": {},
   "outputs": [],
   "source": [
    "vidcom_data['Video_Length_Category'] = pd.Categorical(vidcom_data['Video_Length_Category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "c86e504d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/qt/nkv93n510wlcddjjxc58klyr0000gn/T/ipykernel_4780/2817792131.py:1: DeprecationWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, pd.CategoricalDtype) instead\n",
      "  print(pd.api.types.is_categorical_dtype(vidcom_data['Video_Length_Category']))\n"
     ]
    }
   ],
   "source": [
    "print(pd.api.types.is_categorical_dtype(vidcom_data['Video_Length_Category']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "57b270d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['long', 'medium', 'unknown', 'short', 'super long']\n",
       "Categories (5, object): ['long', 'medium', 'short', 'super long', 'unknown']"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vidcom_data['Video_Length_Category'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "aee6f7b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "length_dummies = pd.get_dummies(vidcom_data['Video_Length_Category'],  prefix='Length',drop_first=True,dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6cc68dd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 44803 entries, 0 to 44802\n",
      "Data columns (total 4 columns):\n",
      " #   Column             Non-Null Count  Dtype\n",
      "---  ------             --------------  -----\n",
      " 0   Length_medium      44803 non-null  int64\n",
      " 1   Length_short       44803 non-null  int64\n",
      " 2   Length_super long  44803 non-null  int64\n",
      " 3   Length_unknown     44803 non-null  int64\n",
      "dtypes: int64(4)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "length_dummies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "e981d187",
   "metadata": {},
   "outputs": [],
   "source": [
    "length_dummies = length_dummies.drop(columns=['Length_unknown'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "7aba4b5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "vidcom_concat = pd.concat([vidcom_data, length_dummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "bf6e717b",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = ['Log_Comments', 'Interaction_Term', 'Published_Year'] + list(length_dummies.columns)\n",
    "X5 = vidcom_concat[predictors]\n",
    "X5 = sm.add_constant(X5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d006a6ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.584665\n",
      "         Iterations 8\n"
     ]
    }
   ],
   "source": [
    "logit_model5 = sm.Logit(vidcom_concat['Popular'], X5).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "c0cff592",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                Popular   No. Observations:                44803\n",
      "Model:                          Logit   Df Residuals:                    44796\n",
      "Method:                           MLE   Df Model:                            6\n",
      "Date:                Wed, 25 Dec 2024   Pseudo R-squ.:                  0.1406\n",
      "Time:                        00:34:37   Log-Likelihood:                -26195.\n",
      "converged:                       True   LL-Null:                       -30479.\n",
      "Covariance Type:            nonrobust   LLR p-value:                     0.000\n",
      "=====================================================================================\n",
      "                        coef    std err          z      P>|z|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------\n",
      "const              -303.1090     10.916    -27.766      0.000    -324.505    -281.713\n",
      "Log_Comments          0.3170      0.009     36.638      0.000       0.300       0.334\n",
      "Interaction_Term      0.1172      0.009     13.428      0.000       0.100       0.134\n",
      "Published_Year        0.1484      0.005     27.480      0.000       0.138       0.159\n",
      "Length_medium         2.0215      0.030     67.259      0.000       1.963       2.080\n",
      "Length_short          1.9400      0.045     43.273      0.000       1.852       2.028\n",
      "Length_super long     3.8950      0.074     52.374      0.000       3.749       4.041\n",
      "=====================================================================================\n"
     ]
    }
   ],
   "source": [
    "print(logit_model5 .summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "417a70f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "log_odds_const5 = logit_model5.params['const']\n",
    "log_odds_comments5 = logit_model5.params['Log_Comments']\n",
    "log_odds_year5 = logit_model5.params['Published_Year']\n",
    "log_odds_int5 = logit_model5.params['Interaction_Term']\n",
    "log_odds_lm5 = logit_model5.params['Length_medium']\n",
    "log_odds_ls5 = logit_model5.params['Length_short']\n",
    "log_odds_lsl5 = logit_model5.params['Length_super long']\n",
    "\n",
    "odds_const5 = np.exp(log_odds_const5)\n",
    "odds_comments5 = np.exp(log_odds_comments5)\n",
    "odds_year5 = np.exp(log_odds_year5)\n",
    "odds_int5 = np.exp(log_odds_int5)\n",
    "odds_lm5 = np.exp(log_odds_lm5)\n",
    "odds_ls5 = np.exp(log_odds_ls5)\n",
    "odds_lsl5 = np.exp(log_odds_lsl5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "fa0c31ce",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept Odds: 0.0000\n",
      "Comments Odds: 1.3730\n",
      "Year Odds: 1.1600\n",
      "Interaction Term Odds: 1.1243\n",
      "Length Medium Odds: 7.5495\n",
      "Length Short Odds: 6.9590\n",
      "Length Super Long Odds: 49.1583\n"
     ]
    }
   ],
   "source": [
    "print(f\"Intercept Odds: {odds_const5:.4f}\")\n",
    "print(f\"Comments Odds: {odds_comments5:.4f}\")\n",
    "print(f\"Year Odds: {odds_year5:.4f}\")\n",
    "print(f\"Interaction Term Odds: {odds_int5:.4f}\")\n",
    "print(f\"Length Medium Odds: {odds_lm5 :.4f}\")\n",
    "print(f\"Length Short Odds: {odds_ls5 :.4f}\")\n",
    "print(f\"Length Super Long Odds: {odds_lsl5 :.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "c6b7a252",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentage_const5 = round((odds_const5 - 1) * 100, 2)\n",
    "percentage_comments5 = round((odds_comments5 - 1) * 100, 2)\n",
    "percentage_year5 = round((odds_year5 - 1) * 100, 2)\n",
    "percentage_int5 = round((odds_int5 - 1) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "0aad460d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept Percentage Change: -100.0\n",
      "Comments Percentage Change: 37.3\n",
      "Year Percentage Change: 16.0\n",
      "Interaction Percentage Change: 12.43\n"
     ]
    }
   ],
   "source": [
    "print(f\"Intercept Percentage Change: {percentage_const5}\")\n",
    "print(f\"Comments Percentage Change: {percentage_comments5}\")\n",
    "print(f\"Year Percentage Change: {percentage_year5}\")\n",
    "print(f\"Interaction Percentage Change: {percentage_int5}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfcda05e",
   "metadata": {},
   "source": [
    "**Interpretation of Regression Coefficients:**\n",
    "\n",
    "1. **Intercept (-314.2358):**\n",
    "   - The intercept represents the baseline log-odds of a video being classified as popular (`Popular`) when all predictors are at their reference levels. The p-value=0.000>0.001 indicates that this relationship is statistically significant, but it is not particularly meaningful for interpretation due to its role as a baseline adjustment. \n",
    "   \n",
    "&NewLine;\n",
    "\n",
    "2. **Log Comments (0.3170):**\n",
    "    - For every unit increase in the log-transformed number of comments(`Log Comments`), the log-odds of a video being popular increase by 0.3170. This corresponds to a 37.3% increase in the odds of popularity for each additional unit of `Log Comments`. The p-value=0.000>0.001 indicates that this relationship is statistically significant, emphasizing the importance of comments in driving video popularity.\n",
    "    \n",
    "&NewLine;\n",
    "\n",
    "3. **Published_Year (0.1484):**\n",
    "    - For every additional year (`Published_Year`) the log-odds of a video being classified as popular (`Popular`) increases by  0.1484., corresponding to a 16.0% increase in the odds of popularity for each additional year. The p-value=0.000<0.001 confirms that this relationship is statistically significant, highlighting a meaningful temporal trend in video popularity or may reflect changes in audience preferences, platform algorithms, or other temporal factors.\n",
    "    \n",
    "&NewLine;\n",
    "    \n",
    "4. **Interaction Term (Log Comments x Replies Presence (0.1172):**\n",
    "    - The interaction between `Log Comments` and  `Replies Presence` indicates that for every one-unit increase in the interaction term, the log-odds of a video being popular (`Popular`) increase by 0.1172, corresponding to a 12.43% increase in the odds of popularity when replies amplify the effect of comments. The p-value=0.000<0.001 confirms that this interaction is statistically significant, showcasing the enhanced importance of comments when replies are present.\n",
    "    \n",
    "&NewLine;\n",
    "    \n",
    "5. **Length medium  (2.0215):**\n",
    "    - Medium-length videos are approximately 7.5 times (odds=7.5495) more likely to be popular compared to videos in the reference category.The p-value=0.000<0.001 confirms its statistical significance.\n",
    "    \n",
    "&NewLine;\n",
    "    \n",
    "6. **Length short  (1.9400):**\n",
    "    - Short-length videos are approximately 7 times (odds=6.9590) more likely to be popular compared to the reference category.The p-value=0.000<0.001 confirms its statistical significance.\n",
    "    \n",
    "&NewLine;\n",
    "    \n",
    "7. **Length super long (3.8950):**\n",
    "    - Super-long videos are approximately 49 times (odds=49.1583) more likely to be popular compared to the reference category. The p-value=0.000<0.001 confirms its statistical significance.\n",
    "   \n",
    "&NewLine;\n",
    "   \n",
    "**Pseudo $R^2$ (0.1406):**\n",
    "    - The $R^2$ that the model explains about 14.06%% of the variance in popularity.This is a small effect, suggesting that while the included predictors contribute meaningfully, other factors not captured in the model likely play a substantial role in determining video popularity."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "389f6211",
   "metadata": {},
   "source": [
    "The inclusion of video length categories significantly improves the model’s explanatory power, with the pseudo $R^2$ increasing from 0.01185 to 0.1406. Futhermore, in the simpler model `Log_Comments` was not significant, but when video length categories are included, it becomes highly significant, highlighting its importance in combination with other predictors.\n",
    "\n",
    "Also, all categories significantly increase the odds of popularity compared to the reference category, and specifically super-long videos show the strongest effect, with odds approximately 49 times higher than the reference category."
   ]
  }
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