Rahim-444/Algerian_car_price_predictor

📌Ai that predicts any car's price using real Algerien data, easy to use with a sleek easy to use interface # 🚗 Car Price Prediction Project ## Overview This project aims to predict the prices of cars using machine learning (ML) and deep learning (DL) techniques. The project utilizes a comprehensive dataset containing features of various cars such as make, model, year, mileage, and other relevant attributes. We have implemented both traditional ML models and DL models to predict car prices accurately. ## ✨ Features - **Machine Learning Models**: - Implemented various ML algorithms including linear regression, random forest, and gradient boosting. - Utilized techniques like feature engineering, feature scaling, and hyperparameter tuning to improve model performance. - **Deep Learning Models**: - Developed neural network architectures using libraries like TensorFlow and Keras. - Explored different architectures including feedforward neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). - Employed techniques like dropout regularization and batch normalization to prevent overfitting. - **Full-Stack Implementation**: - Developed a web application using the Next.js framework for the frontend. - Integrated the ML and DL models into the backend using Flask or FastAPI. - Implemented a user-friendly interface for users to input car features and receive predicted prices. ## 🗂️ Project Structure The project is organized into the following directories: - **data/**: Contains the dataset used for training and testing the models. - **notebooks/**: Jupyter notebooks used for exploratory data analysis (EDA), model development, and evaluation. - **models/**: Saved trained models. - **src/**: Source code for the web application. - **backend/**: Backend code implementing the ML and DL models. - **frontend/**: Frontend code for the Next.js application. ## 🚀 Usage 1. **Setup Environment**: - Install required dependencies using `pip install -r requirements.txt`. - Ensure Node.js and npm are installed for the frontend setup. 2. **Training Models* …