2024GR26CS462_5G-Data-Predictor-Xgboost

The 5G Data Usage Prediction App is a machine learning-based web application that predicts the amount of data usage based on user behavior, network conditions, and device types. The app is built using Streamlit for an interactive user interface and utilizes an XGBoost model for accurate predictions.

📌 Features

• ✅ Predicts mobile data usage based on real-world parameters
• ✅ Interactive UI built with Streamlit
• ✅ Supports categorical and numerical inputs for accurate modeling
• ✅ Uses XGBoost, a high-performance machine learning model
• ✅ Live deployment capability with Streamlit Cloud

📂About dataset i have created my own dataet Overview of My Dataset My dataset contains information about 5G data usage based on different factors. Here’s a breakdown of the key columns:

📊 Dataset Features

1️⃣ User Information

• user_id → Unique identifier for each user.

2️⃣ Device & Application Usage

• device_type → Type of device (Smartphone, Laptop, Tablet, etc.).
• app_category → Category of application (Gaming, Browsing, Video Streaming, etc.).
• session_duration → Duration of the session in minutes.
• data_quality → Quality of data used (Low, High, Ultra-HD, etc.).

3️⃣ Network & Connection Details

• time_of_day → Session time (Morning, Afternoon, Night, etc.).
• day_of_week → Day when the session occurred.
• network_type → Type of network (5G NSA, 5G SA, mmWave, Sub-6GHz, etc.).
• signal_strength → Signal strength in dBm (Negative values indicate weaker signals).
• prev_usage → Previous data usage in MB/GB.

4️⃣ Location & Environmental Factors

• location_type → Whether the user is in a Suburban, Urban, or Rural area.
• indoor_outdoor → Indicates if the session was indoors or outdoors.

5️⃣ Performance & Data Consumption

• throughput → Network speed in Mbps.
• background_usage → Background data consumption in GB.
• data_usage → The actual data usage in MB/GB (Target variable for prediction).

Technologies Used

Python (Machine Learning & Backend)

Streamlit (Frontend & Deployment)

XGBoost (Model Training)

Pandas & NumPy (Data Processing)

Plotly (Visualization)

web-app interface

Git-clone:by uing thi link: git clone https://github.com/ujwalreddybattu04/5G-Data-Predictor-Xgboost.git

🌐 Live Demo The 5G Data Usage Prediction App is now live on Streamlit Cloud! 🚀 🔗 Access the app here: 👉https://github.com/ujwalreddybattu04/2024GR00CS462_5G-Data-Predictor-Xgboost

The 5G Data Usage Prediction Model is built using XGBoost, a high-performance machine learning algorithm optimized for structured data. The model was trained and evaluated using multiple metrics to ensure high accuracy. The model achieves exceptional accuracy, with an R² score of 0.9996, indicating near-perfect correlation between input factors and predicted data usage. The low MAE and RMSE further validate the model’s precision in forecasting mobile data consumption. MAE:31.4670 RMSE:47.0258

References: https://ieeexplore.ieee.org/document/10048885 https://www.sciencedirect.com/science/article/abs/pii/S1051200423004542