This project is focused on implementing ML Ops for car price prediction. The main goal is to build a pipeline that automates the process of model training, evaluation, and prediction, while integrating essential ML Ops practices like logging, error handling, and version control for models.
- Introduction
- Project Overview
- Tools Used
- Getting Started
- Screenshots
- Use Case
- Future Improvements
- License
This repository contains a machine learning project aimed at predicting car prices using a combination of ML models and ML Ops practices. The project is structured to focus on continuous integration and deployment (CI/CD) for ML models, making it an example of good ML Ops practices.
The goal of this project is to predict car prices based on various features such as car make, model, year, mileage, etc. It also focuses on the use of ML Ops tools and pipelines to manage the model lifecycle, including model training, monitoring, and deployment.
- ML Pipeline: A series of stages to load data, preprocess it, train models, and make predictions.
- Model Management: Proper management of trained models using
joblibfor saving and loading models. - Logging and Exception Handling: Robust logging and error handling during data ingestion, transformation, and training.
- Python: Primary language for implementing the ML model and pipelines.
- scikit-learn: For implementing the machine learning model (e.g., Linear Regression).
- joblib: For saving and loading the model.
- Pandas: For data processing.
- Matplotlib/Seaborn: For data visualization in the notebooks.
- Docker: For containerizing the application and ensuring environment consistency.
- Logging: For tracking and debugging the pipeline processes.
- Custom Exceptions: For error handling and improving debugging.
To get started with this project, follow these steps:
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Clone the Repository:
git clone https://github.com/abhi227070/Car-Price-Prediction-With-MLOps.git cd Car-Price-Prediction-With-MLOps -
Install Dependencies: Make sure you have Python 3.7+ installed, then use the requirements.txt file to install all necessary dependencies
pip install -r requirements.txt
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Running the Application: To run the application, execute the app.py file
python app.py
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Training the Model: To train the model, you need to run the training pipeline script
python src/car_price_prediction/pipelines/training_pipeline.py
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Making Predictions: To make predictions using the trained model, run the prediction pipeline
python src/car_price_prediction/pipelines/prediction_pipeline.py
Below are some screenshots of the project in action:
The Car Price Prediction using ML Ops project can be used in various real-world scenarios, including:
Car dealerships can use the model to predict the selling price of used cars based on various factors like make, model, year, mileage, and other relevant features. This helps dealerships in pricing cars competitively and profitably.
Online platforms that list cars for sale can use the model to automatically suggest the fair price of cars to both buyers and sellers. This ensures transparency and helps users get a quick estimate of car values.
Insurance companies can use the model to assess car prices when determining the premium amounts for vehicle insurance. The model’s predictions can help insurance companies more accurately determine vehicle values during claim processing.
Financial institutions offering auto loans can use the car price prediction model to evaluate the value of cars that applicants want to finance. This ensures that the loan amounts are in line with the actual market value of the cars.
Car manufacturers can use the model to predict the potential resale value of their cars in the used-car market. This can help them optimize production and marketing strategies by identifying the price elasticity of their cars.
This car price prediction model offers value across different stakeholders in the automotive industry, including buyers, sellers, insurers, and financial service providers.
While the current version of the Car Price Prediction using ML Ops project provides valuable insights, there are several potential improvements that can be made to enhance its functionality and usability:
- Experiment with different machine learning algorithms (e.g., Random Forest, XGBoost) to compare model accuracy and performance.
- Implement hyperparameter tuning techniques like Grid Search or Random Search to optimize the model’s performance.
- Consider using ensemble learning techniques to improve prediction accuracy.
- Develop a real-time prediction API that allows users to input car details and receive price predictions instantly.
- Implement auto-scaling for handling high traffic and multiple simultaneous requests.
- Incorporate more features into the dataset, such as the car’s condition, accident history, and geographical location, to improve the model's accuracy.
- Add external data sources like market trends and inflation rates to make predictions more dynamic and adaptive to changing conditions.
- Implement a model drift detection system that monitors the model's performance over time and triggers retraining if needed.
- Set up automated alerts for model drift, so the system can notify stakeholders about performance degradation.
- Create an automated data pipeline that continuously ingests new data from various sources (e.g., web scraping, external APIs) to keep the model up-to-date with the latest car market trends.
- Ensure the data pipeline handles new feature additions and updates to existing features seamlessly.
- Develop a user-friendly web application or dashboard where users can easily input car details and view the predicted price.
- Allow users to visualize data and model performance through interactive graphs and charts.
- Deploy the application to the cloud using services like AWS, Google Cloud, or Azure to improve scalability, accessibility, and maintenance.
- Implement continuous integration and continuous deployment (CI/CD) pipelines for automated testing and deployment.
- Integrate model interpretability tools (e.g., SHAP, LIME) to provide users with insights into how the model is making predictions.
- This will help increase transparency and trust in the model’s predictions.
These future improvements will help enhance the model’s performance, scalability, and user experience, making it a more robust solution for real-world applications.
This project is licensed under the MIT License - see the LICENSE file for details.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is provided to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.