himanshu-03 / Device-Failure-Analysis

To build a predictive model using machine learning to predict the probability of a device failure. When building this model, be sure to minimize false positives and false negatives. The column you are trying to Predict is called failure with binary value 0 for non-failure and 1 for failure.

Maintenance Cost Reduction through Predictive Techniques

📌 Problem Definition

A company has a fleet of devices transmitting daily sensor readings. They would like to create a predictive maintenance solution to proactively identify when maintenance should be performed. This approach promises cost savings over routine or time based preventive maintenance, because tasks are performed only when warranted.

👀 Screenshots

🎯 Goal

The task is to build a predictive model using machine learning to predict the probability of a device failure. When building this model, be sure to minimize false positives and false negatives. The column you are trying to Predict is called failure with binary value 0 for non-failure and 1 for failure.

📓 Overview

Machine Learning Models Applied	Accuracy
Logistic Regression	92.59%
Logistic Regression with Hyperparameter Tuning	93.16%
K - Nearest Neighbour	94.87%

✍️ Authors

• Himanshu Agarwal
• Yash Jain
• Shiv Dixit

🔗 Links

🪪 License

This project follows the MIT LICENSE.

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