Machine Failure Prediction Model

Introduction

This project aims to predict machine failures using machine learning techniques. The model, developed using Python in a Jupyter Notebook environment, utilizes a combination of Keras, TensorFlow, and scikit-learn libraries for data preprocessing, model construction, training, and evaluation.

Dependencies

• pandas
• scikit-learn
• numpy
• TensorFlow
• Keras

Dataset

The dataset used in this project is stored in a CSV file named "machine failure.csv". It consists of various features such as UDI, Product ID, Type, Air temperature, Process temperature, Rotational speed, Torque, Tool wear, and multiple failure categories.

Usage

1. Clone the Repository: Clone this repository to your local machine.

2. Install Dependencies: Ensure you have all the required dependencies installed. You can install them using pip:

   pip install pandas scikit-learn numpy tensorflow keras
   

3. Open Jupyter Notebook: Open the Jupyter Notebook file ProjectCode.ipynb in your Jupyter Notebook environment.

4. Run the Notebook: Execute each cell in the notebook sequentially to load the dataset, preprocess the data, build the machine failure prediction model, train the model, and evaluate its performance.

5. Evaluate Results: After running all the cells, review the output to observe the model's accuracy on the validation and test sets.

Model Architecture

The neural network model architecture consists of:

• Input layer with 11 neurons (input features)
• Two hidden layers with 7 and 3 neurons, respectively, using ReLU activation function
• Output layer with 1 neuron using Sigmoid activation function for binary classification

Performance

• Validation Set Accuracy: 99.9%
• Test Set Accuracy: 99.85%

Future Improvements

• Experiment with different neural network architectures and hyperparameters to potentially improve model performance.
• Explore additional feature engineering techniques to enhance predictive capabilities.

License

This project is licensed under the MIT License.