This project aims to develop a machine learning model for estimating repair durations for mobile phones based on various factors such as phone brand, model, repair problem, and date received. The model helps mobile phone repair services optimize their repair processes and provide accurate repair time estimates to customers.
- Predict repair durations for mobile phones based on historical repair data.
- Visualize the distribution of repair duration categories by phone brand and repair problem.
- Evaluate model performance using classification metrics such as accuracy, precision, recall, and F1-score.
- Explore feature importance to understand factors influencing repair durations.
- Provide insights and recommendations for improving repair processes and customer satisfaction.
- Clone the repository: git clone https://github.com/nikolamurgo/mobile-phone-repair-estimation.git
- Prepare your dataset: Ensure your dataset is in the required format (CSV, Excel, etc.) and contains relevant features such as phone brand, model, repair problem, date received, and repair duration.
- Train the model: Run the provided Python scripts to preprocess the data, train the machine learning model, and evaluate its performance.
- Visualize results: Use the provided visualization scripts to generate plots and charts for analyzing repair duration distributions by phone brand and repair problem.
- Interpret results: Analyze model performance metrics, feature importance, and other insights to understand factors influencing repair durations and identify areas for improvement.
This project is licensed under the MIT License - see the LICENSE file for details.