Web Traffic Prediction

Overview

This project focuses on predicting sustained high web traffic for specific web pages using machine learning techniques. It involves data preprocessing, feature extraction with TF-IDF and Word2Vec, and the utilization of models like Logistic Regression, Random Forest Classifier, SVM, and K-Nearest Neighbors classifier.

Methodology

• Data Preprocessing: Clean and preprocess data for model suitability.
• Feature Extraction: Utilize TF-IDF and Word2Vec techniques for feature generation from textual data.
• Model Implementation: Employ various models for prediction.

Instructions

Setup

• Ensure necessary libraries (scikit-learn, NLTK, etc.) are installed.
• Download the dataset and place it in the designated folder.

Execution

• Run provided scripts/notebooks for data preprocessing, feature extraction, and model implementation.

Evaluation

• Assess model performance using accuracy, precision, recall, and F1-score.

Conclusion

This project aims to predict sustained high web traffic for specific web pages using machine learning. Experiment with different models and parameters for optimal predictions.

Contributors

• Adwait Upadhyay
• Ajay kumar