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
🔗 Links
🪪 License
This project follows the MIT LICENSE.
