Welcome to the Vehicle Coupon Recommendation project repository! This project leverages Exploratory Data Analysis (EDA), machine learning, and Streamlit to provide a powerful recommendation system for vehicle owners. It helps users find the best coupons and deals tailored to their specific vehicle maintenance needs.
Coupon systems have been widely used to market products, and services and engage customers to use their products and services often. Coupons create a win-win situation for both companies and customers so, by offering a correct coupon to users, which can lead users to become frequent customers and it is enhancing a brand’s impact on its customers.
How to know which coupon to provide a customer can be a rather complex task, since each customer profile responds differently to each other, and frequently offering them bad coupons or deals might drag them away from your business. To overcome this problem, machine learning techniques can be used to build a better coupon recommendations system.
Are you looking for the most cost-effective way to maintain and service your vehicle? This project has got you covered. Here's what you can expect:
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Exploratory Data Analysis (EDA): Our team conducted extensive EDA on vehicle maintenance data, extracting valuable insights. We analyzed user preferences, service patterns, and coupon usage trends. This EDA was instrumental in shaping our recommendation engine.
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Machine Learning Model: The heart of our system is a sophisticated machine learning model. It takes into account vehicle data, user preferences, and the findings from our EDA to deliver highly personalized coupon recommendations. Say goodbye to generic coupons that don't suit your needs.
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Streamlit Deployment: We've deployed the model using Streamlit, providing an intuitive web interface. In real-time, the system generates personalized coupon recommendations. It's as easy as inputting your vehicle details and preferences.
We delved deep into the data to understand vehicle maintenance habits and user preferences. Here are some of the insights we gained from our EDA:
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Popular Maintenance Services: We identified the most sought-after maintenance services, helping us prioritize the recommendations for these services.
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User Behavior Patterns: Through EDA, we observed how users typically respond to coupons and deals, enabling us to fine-tune our recommendations.
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Geographic Variation: Certain regions may have unique vehicle maintenance needs. Our EDA considered geographical variations to offer location-specific recommendations.
Our recommendation engine is powered by a robust machine learning model. Here's what makes it special:
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Personalization: The model tailors coupon recommendations to individual users based on their vehicle make, model, past maintenance, and preferences. No two users receive the same recommendations.
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Real-time Updates: The model is designed for real-time operation, ensuring that recommendations stay relevant and up-to-date.
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Cost Savings: By providing users with coupons and deals that match their specific needs, the model helps users optimize their vehicle maintenance expenses.
Contributions to this project are welcome. If you would like to contribute, please follow these steps:
- 1)Create a new branch from the
mainbranch to work on your changes. - 2)Make your modifications and commit your changes.
- 3)Push your branch to your forked repository.
- 4)Open a pull request to the original repository, describing the changes you made.
This project is licensed under the GPU License.
If you have any questions or suggestions regarding this project, please feel free to contact me at 132anaskhan@gmail.com.