YouTube Video Popularity Predictor – IT and Computer Engineering Guide
1. Project Overview
Objective: Predict the popularity of YouTube videos in terms
of views or likes based on video metadata and features.
Scope: Help content creators optimize video performance and provide insights
into factors influencing popularity.
2. Prerequisites
Knowledge: Regression techniques, data preprocessing,
feature engineering, and evaluation metrics.
Tools: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, and optionally
TensorFlow or PyTorch.
Data: Dataset with video features like title, tags, duration, category, and
number of views or likes.
3. Project Workflow
- Data Collection: Obtain video metadata and statistics from the YouTube API or public datasets.
- Data Preprocessing: Clean and preprocess the data by handling missing values and encoding categorical features.
- Feature Engineering: Extract useful features like video length, keyword density, or engagement ratio.
- Model Training: Train regression models like Linear Regression, Random Forest, or Neural Networks.
- Evaluation: Assess model performance using metrics like Mean Absolute Error (MAE) and R² score.
- Deployment: Integrate the model into a web application or dashboard for real-time predictions.
4. Technical Implementation
Step 1: Import Libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, r2_score
Step 2: Load and Preprocess Data
# Load dataset
data = pd.read_csv('youtube_data.csv')
# Handle missing values
data.fillna({'tags': '', 'category': 'Unknown'}, inplace=True)
# Encode categorical variables
data = pd.get_dummies(data, columns=['category'], drop_first=True)
# Feature selection
selected_features = ['duration', 'likes', 'comments', 'shares']
X = data[selected_features]
y = data['views']
Step 3: Train-Test Split
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
Step 4: Train the Model
# Train a Random Forest Regressor
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
Step 5: Evaluate the Model
# Make predictions
y_pred = model.predict(X_test)
# Evaluate model performance
print('MAE:', mean_absolute_error(y_test, y_pred))
print('R² Score:', r2_score(y_test, y_pred))
Step 6: Feature Importance
# Plot feature importance
importances = model.feature_importances_
plt.bar(selected_features, importances)
plt.xlabel('Features')
plt.ylabel('Importance')
plt.title('Feature Importance')
plt.show()
5. Results and Insights
Analyze which features contribute most to predicting video popularity. Provide actionable insights for content optimization.
6. Challenges and Mitigation
Imbalanced Data: Address underrepresented categories or
metrics.
Overfitting: Apply techniques like cross-validation and regularization.
7. Future Enhancements
Incorporate advanced features like thumbnail analysis or
audience retention metrics.
Experiment with deep learning models for improved prediction accuracy.
8. Conclusion
The YouTube Video Popularity Predictor uses machine learning to provide actionable insights for optimizing video content and maximizing reach.