🛠️ Machine Failure Prediction - README

📁 Project Title:

Machine Failure Prediction using Random Forest

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📋 Description:

This project aims to predict machine failures in advance using sensor data collected from machines. The dataset includes readings like temperature, air quality, VOCs, electrical current, etc. A machine learning model is trained to classify whether a machine is likely to fail (1) or not (0).

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🧾 Dataset Overview:

Filename: data (1).csv

Column	Description
footfall	Number of people or objects passing by the machine
tempMode	Temperature setting of the machine
AQ	Air Quality index
USS	Ultrasonic sensor value
CS	Current sensor value
VOC	Volatile Organic Compound level
RP	Rotational position or RPM
IP	Input pressure
Temperature	Operating temperature
fail	Target variable (1 = machine failed, 0 = working)

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📌 Objective:

To build a machine learning model that predicts the fail column using the sensor features, helping to prevent costly machine downtimes.

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🛠️ Technologies Used:

• Python 3.x
• pandas
• scikit-learn
• matplotlib
• seaborn

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🧠 Machine Learning Model:

Algorithm: Random Forest Classifier
Train/Test Split: 80/20
Accuracy Achieved: ~87.8%

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📊 Feature Importance Plot:

The Random Forest model ranks each sensor feature based on its contribution to predictions.
📁 File: feature_importance_plot.png

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📁 File Structure:

├── data (1).csv                 # Dataset
├── feature_importance_plot.png # Feature importance bar chart
├── machine_failure_model.py    # Python code for model training
├── README.md                   # This file

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🚀 How to Run:

1. Install required libraries:

   pip install pandas scikit-learn matplotlib seaborn

2. Run the script:

   python machine_failure_model.py

3. View output in the terminal and graph in feature_importance_plot.png.

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✅ Future Improvements:

• Try different models (e.g., XGBoost, SVM).
• Perform hyperparameter tuning.
• Build a real-time dashboard or API with Flask.
• Implement alerting or monitoring based on predictions.