{"id":25926808,"url":"https://github.com/mainakverse/automart","last_synced_at":"2026-04-18T10:38:55.033Z","repository":{"id":280488364,"uuid":"942165847","full_name":"MainakVerse/AutoMart","owner":"MainakVerse","description":"AutoVault is your advanced car valuation platform powered by machine learning. 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By leveraging a deep learning model built with **TensorFlow**, this app estimates car prices based on various input features like brand, location, mileage, engine specifications, and more. With **Streamlit** as the frontend, the app provides a seamless and interactive user experience to estimate the value of any used car in real-time.\n\n---\n\n## 📦 **Technologies Used**\n\n- **Python**: The main programming language used to build the app.\n- **TensorFlow**: Deep learning framework for training the car price prediction model.\n- **Streamlit**: Interactive web framework for building and deploying the user interface.\n- **Scikit-learn**: Used for preprocessing the data and splitting it into training and testing sets.\n- **Pandas \u0026 NumPy**: For efficient data manipulation and numerical computations.\n- **Plotly**: For visualizing model training progress and evaluation metrics.\n\n---\n\n\n## 🚀 **Usage**\n\n1. **Run the Streamlit app**:\n\n   Launch the app by clicking:\n\n   \n2. **Enter Car Details**:\n\n   Use the intuitive UI to input details about the car:\n   - **Location**: Choose the car’s location from a dropdown.\n   - **Fuel Type**: Select the car's fuel type (e.g., Petrol, Diesel, Electric).\n   - **Transmission**: Choose between Automatic or Manual transmission.\n   - **Owner Type**: Select whether the car is being sold by the first, second, or third owner.\n   - **Car Features**: Enter the car's year of manufacture, mileage, engine capacity (CC), power (bhp), and number of seats.\n\n3. **Get Predicted Price**:\n\n   After entering the details, click **\"Predict Car Sale Value\"** and watch the app instantly generate a predicted sale price for the car!\n\n---\n\n\n## 🔧 **Model Overview**\n\nThe car sale price prediction model is built using a **Multi-Layer Perceptron (MLP)** regressor neural network. Here’s a breakdown of the model:\n\n- **Model Type**: MLP Regressor (Deep Neural Network).\n- **Loss Function**: Mean Squared Error (MSE) for regression tasks.\n- **Metrics**: Model evaluation is done using MAE (Mean Absolute Error), MSE, RMSE, and R² score.\n\n---\n\n## 🗂️ **Project Structure**\n\nHere’s how the project is organized:\n\n```\n├── app.py                  # Streamlit app (main entry point)\n├── model                   # Folder containing saved model files (e.g., tensflow.joblib)\n├── requirements.txt        # List of required Python libraries\n├── README.md               # This file (project description and setup guide)\n├── train-data.csv          # Dataset used for training the model\n├── test-data.csv           # Dataset used for testing the model\n└── utils                   # Utility functions for data preprocessing\n```\n\n---\n\n## 📊 **Model Evaluation**\n\nAfter training the model, we evaluate its performance using the following metrics:\n\n- **Mean Absolute Error (MAE)**: Measures the average magnitude of errors in the predictions.\n- **Mean Squared Error (MSE)**: Measures the average squared difference between predicted and actual values.\n- **Root Mean Squared Error (RMSE)**: Square root of MSE, a more interpretable version.\n- **R² Score**: Indicates how well the model explains the variance in the target data (Car Sale Price).\n\n---\n\n## 🧑‍💻 **Contributing**\n\nWe welcome contributions! Feel free to:\n- **Fork** the repository.\n- **Make changes** or improvements to the code.\n- **Submit a pull request** with your changes.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmainakverse%2Fautomart","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmainakverse%2Fautomart","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmainakverse%2Fautomart/lists"}