Predicting Drive Failure

Project Overview: Predicting Drive Failure focuses on using machine learning techniques to anticipate hard drive failures before they occur. By analyzing key SMART attributes and capacity metrics, the project enables proactive maintenance and reduces data loss risks. Multiple models were explored for classification and regression tasks, providing insights into the most effective predictive approaches.

Project source code: https://github.com/Risdorn/Predicting-Drive-Failure

Objectives

1. Build a predictive system to forecast hard drive failures using historical SMART data.
2. Evaluate multiple machine learning models for classification and regression accuracy.
3. Provide actionable insights for early intervention to prevent data loss.

Features

1. Data Processing: Utilizes BackBlaze 2016 hard drive dataset including key SMART metrics.
2. Failure Prediction: Models predict drive failure based on metrics like Reallocated Sectors Count, Command Timeout, and Current Pending Sectors.
3. Multi-Model Approach: Implements several machine learning models through PyCaret for comparison and robustness.
4. Regression and Classification: Supports both predicting days till failure (regression) and failure/no-failure status (classification).

Technology Stack

• Language: Python
• Libraries: PyCaret, Pandas, NumPy, Scikit-learn
• Data Source: BackBlaze Hard Drive Test Data (2016)
• Tools: Jupyter Notebook / Google Colab for experimentation and visualization

Outcome

The project demonstrates the capability to predict hard drive failures accurately using SMART data. It enables proactive maintenance strategies and reduces the risk of data loss, while providing insights into which machine learning models and features are most predictive for this problem.