Leveraging Predictive Analytics of Age Demographics for Engagement and Revenue Forecasting

This Research introduces a data-driven approach utilizing machine learning to predict income based on age. Leveraging the Python programming language and libraries such as pandas and sci-kit-learn, the model employs a Gradient Boosting Classifier, Random Forest Classifier, XGBoost Classifier, Support Vector Classifier and Neural Network Classifier. to discern patterns from a provided dataset. The dataset, representative of individual age-income relationships, is split into training and testing subsets to facilitate model validation. Employing hyperparameter tuning, the Random Forest Regressor is trained on the training subset to optimize predictive accuracy. User interaction is integrated, allowing for age input for real-time income prediction. The model’s performance is assessed using an evaluation metric. This research demonstrates the feasibility of employing machine learning techniques for predicting income based on demographic attributes, with potential applications in financial planning and market analysis.] This paper introduces a novel method using machine learning to predict website engagement based on user age. Implemented in Python with NumPy for efficient array operations, the algorithm prompts users to input their age, validating it within 18 to 64 years. Error handling ensures data integrity. The system encapsulates the age input in a NumPy array, feeding it into a pre-trained model to predict website engagement time. The model leverages prior training data to make predictions, providing valuable insights into user behaviour. It gracefully handles age inputs outside the dataset’s range, acknowledging model limitations. Future work may refine the model with additional features for improved accuracy. Overall, this methodology demonstrates machine learning’s efficiency with $\mathbf{9 6 \%} \%$ accuracy in predicting user behaviour based on demographic attributes, with potential applications in web design and content optimization.