Traffic Growth Prediction Using Historical Data

Project Overview

This project demonstrates how Machine Learning can be used to predict future telecom traffic growth using historical data. The project focuses on understanding the complete ML workflow including data preparation, model training, prediction, and visualization.

The goal is to show how traffic trends can be analyzed and forecasted to support telecom network planning.

Objective

The objectives of this project are:

• Analyze historical telecom traffic data
• Predict future traffic growth trends
• Visualize actual versus predicted traffic
• Understand the application of Machine Learning in telecom systems

Tools and Technologies Used Platform: Google Colab Programming Language: Python

Libraries:

• Pandas
• NumPy
• Scikit-learn
• Matplotlib

All tools and libraries used in this project are open source.

Dataset Description

The dataset used in this project is synthetically generated to simulate telecom traffic behavior.

Dataset details:

• Type: Synthetic historical traffic data
• Number of records: 2000

Features included:

• active_users: Number of active users
• avg_data_per_user: Average data usage per user in GB
• peak_hour_traffic: Peak hour traffic in GB
• video_streaming: Video streaming traffic in GB
• voice_calls: Voice call traffic
• iot_devices: Number of IoT devices
• network_load: Network utilization percentage
• packet_loss: Packet loss percentage
• latency_ms: Network latency in milliseconds
• day_number: Time index for trend learning

Machine Learning Approach

The following steps were followed:

1. Loaded the dataset using Pandas
2. Performed data cleaning and preprocessing
3. Split the dataset into training and testing sets
4. Trained a Linear Regression model
5. Predicted traffic values on test data
6. Evaluated model performance using Mean Absolute Error and Mean Squared Error
7. Visualized historical data, predicted values, and future traffic trends

Results and Visualization

The trained model successfully learned the traffic growth trend from historical data. Predicted values closely follow actual traffic patterns. Graphs clearly show:

• Historical traffic data
• Model predictions on test data
• Future traffic forecast

Features Implemented

Mandatory features:

• Data loading and preprocessing
• Machine learning model training
• Traffic prediction
• Visualization of results

Optional features:

• User-defined number of future prediction days
• Error calculation (MAE and MSE)
• Clean and readable graphs

Project Structure

traffic-growth-prediction

• traffic_prediction.ipynb
• traffic_data.csv
• README.md
• output_graph.png

Learning Outcomes

After completing this project:

• Understood how Machine Learning can be applied in telecom traffic forecasting
• Learned regression modeling from scratch
• Gained hands-on experience with Google Colab and ML libraries
• Improved skills in data visualization and interpretation

Future Enhancements

• Use advanced regression models
• Include seasonal traffic patterns
• Work with real telecom datasets
• Build a web-based dashboard for visualization