AI for connectivity hackathon 

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Selfcode

Arfah_t

Asma_Zubair

AbdullahBhatti308

Hammadaslam

Network Anomaly Detection & Congestion Prediction System
This tool is designed to help you analyze and visualize your network traffic data to identify unusual patterns (anomalies) and predict potential network congestion. Whether you’re a network admin, a data scientist, or simply curious about traffic analysis, this app has you covered.
Key Features:
Anomaly Detection: Identify abnormal network traffic based on various metrics like packet length, protocol type, and time.
 Congestion Prediction: Predict potential network congestion by analyzing traffic patterns and detecting anomalies.
Interactive Visualizations: Stunning charts and graphs for a more insightful analysis of the data, anomalies, and congestion.
 Technologies Used:
Streamlit: The backbone of this web app, providing an interactive and easy-to-use interface.
 Pandas: For data manipulation and analysis of the network traffic data.
NumPy: For numerical operations and data transformations.
 Plotly: Interactive and dynamic visualizations like histograms, scatter plots, and pie charts.
 Scikit-learn: Machine learning models like Isolation Forest for anomaly detection and congestion prediction.
Python: The main programming language behind the app’s backend logic.
 Steps to Use:
Step 1: Upload your network traffic data (CSV format). 
Step 2: Run Anomaly Detection to detect abnormal traffic patterns. 
Step 3: Predict Network Congestion based on detected anomalies. 
 Visual Enhancements:
 Anomaly Distribution: A histogram to visualize how many records are normal and how many are anomalous.
Anomalies Over Time: Scatter plot shows anomalies in relation to the time of day.
 Anomaly vs Normal: A pie chart to compare the number of normal vs anomalous records.





Network Anomaly Detection & Congestion Prediction System 
This tool is designed to help you analyze and visualize your network traffic data to identify unusual patterns  and predict potential network congestion.

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