AI Log Intelligence & Failure Prediction System

Overview

An end-to-end AI system that analyzes system/application logs to detect anomalies, predict failures, and provide early warnings.
The project integrates machine learning, NLP (TF-IDF), anomaly detection, and time-ahead prediction, deployed via an interactive Streamlit dashboard.

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Features

• Log parsing and feature engineering
• Failure prediction using Logistic Regression
• 30-minute ahead failure prediction
• Anomaly detection using Isolation Forest
• NLP-based log message analysis (TF-IDF)
• Auto-generated incident summary
• Interactive Streamlit dashboard

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System Architecture

Raw Logs ↓ Log Parsing & Cleaning ↓ Feature Engineering (Severity, Error Counts, TF-IDF) ↓ ML Models ├─ Failure Prediction ├─ Time-Ahead Prediction ├─ Anomaly Detection └─ NLP-Based Prediction ↓ Decision Layer & Incident Summary ↓ Streamlit Dashboard

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Models Used

Task	Model
Failure prediction	Logistic Regression
30-min ahead prediction	Logistic Regression
Anomaly detection	Isolation Forest
Log text analysis	TF-IDF + Logistic Regression

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Tech Stack

• Python
• pandas, scikit-learn
• TF-IDF (NLP)
• Streamlit
• Git & GitHub

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How to Run

pip install -r requirements.txt
streamlit run dashboard/app.py