Fix_Ride is a machine learning–driven recommendation engine designed to match users with the most suitable mechanics based on real-world parameters like service type, location (via Haversine distance), user preferences, and interaction history.
FIX_Ride is a smart recommendation system that connects users with the most suitable mechanics based on service type, user preferences, and location, provides servics in emergency cases. It uses a machine learning model built with Keras and TensorFlow, incorporating features like distance, ratings, cost, experience, and response time to personalize mechanic recommendations.
The backend of FIX_Ride is written in Python and includes:
- A recommendation engine that predicts the most suitable mechanics for users.
- Preprocessed datasets of users, mechanics, and service requests.
- Distance calculation using the Haversine formula to find geographic proximity.
- Label encoding and feature scaling for categorical and numerical data.
- A neural network built using Keras and TensorFlow to learn user-mechanic-service interactions.
- TensorFlow – Deep Learning Framework
- Keras – Functional API for building recommendation models
- [Pandas, NumPy] – Data manipulation and numerical operations
- Scikit-learn – Data preprocessing and evaluation
- Haversine – Distance calculation between geolocations
- ✅ Personalized mechanic recommendations using learned embeddings
- ✅ Service type filtering and geolocation-based ranking
- ✅ Normalized numerical features: distance, ratings, cost, experience, response time
- ✅ Built using a custom Neural Collaborative Filtering (NCF)-style architecture with Keras
- Python 3.8+
- TensorFlow 2.x
- Pandas
- NumPy
- scikit-learn
- haversine
git clone https://github.com/anjaliy11/Fix_Ride.git
cd Fix_Ride
pip install -r requirements.txt