https://github.com/mainakverse/automart
AutoVault is your advanced car valuation platform powered by machine learning. Our AI-driven system analyzes various parameters to predict the optimal selling price for your vehicle based on current market trends, vehicle specifications, and location factors.
https://github.com/mainakverse/automart
cars generative-ai machine-learning prediction-model regression streamlit vehicle webapp
Last synced: about 2 months ago
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AutoVault is your advanced car valuation platform powered by machine learning. Our AI-driven system analyzes various parameters to predict the optimal selling price for your vehicle based on current market trends, vehicle specifications, and location factors.
- Host: GitHub
- URL: https://github.com/mainakverse/automart
- Owner: MainakVerse
- Created: 2025-03-03T17:18:25.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-05-13T17:12:29.000Z (5 months ago)
- Last Synced: 2025-05-13T18:28:05.221Z (5 months ago)
- Topics: cars, generative-ai, machine-learning, prediction-model, regression, streamlit, vehicle, webapp
- Language: Jupyter Notebook
- Homepage: https://automart.streamlit.app/
- Size: 2.32 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🚗 AutoMart - Predict Your Car Worth
**AutoMart** is a powerful machine learning web app that predicts the sale value of used cars! 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.
---
## 📦 **Technologies Used**
- **Python**: The main programming language used to build the app.
- **TensorFlow**: Deep learning framework for training the car price prediction model.
- **Streamlit**: Interactive web framework for building and deploying the user interface.
- **Scikit-learn**: Used for preprocessing the data and splitting it into training and testing sets.
- **Pandas & NumPy**: For efficient data manipulation and numerical computations.
- **Plotly**: For visualizing model training progress and evaluation metrics.
---
## 🚀 **Usage**
1. **Run the Streamlit app**:
Launch the app by clicking:
2. **Enter Car Details**:
Use the intuitive UI to input details about the car:
- **Location**: Choose the car’s location from a dropdown.
- **Fuel Type**: Select the car's fuel type (e.g., Petrol, Diesel, Electric).
- **Transmission**: Choose between Automatic or Manual transmission.
- **Owner Type**: Select whether the car is being sold by the first, second, or third owner.
- **Car Features**: Enter the car's year of manufacture, mileage, engine capacity (CC), power (bhp), and number of seats.
3. **Get Predicted Price**:
After entering the details, click **"Predict Car Sale Value"** and watch the app instantly generate a predicted sale price for the car!
---
## 🔧 **Model Overview**
The car sale price prediction model is built using a **Multi-Layer Perceptron (MLP)** regressor neural network. Here’s a breakdown of the model:
- **Model Type**: MLP Regressor (Deep Neural Network).
- **Loss Function**: Mean Squared Error (MSE) for regression tasks.
- **Metrics**: Model evaluation is done using MAE (Mean Absolute Error), MSE, RMSE, and R² score.
---
## 🗂️ **Project Structure**
Here’s how the project is organized:
```
├── app.py # Streamlit app (main entry point)
├── model # Folder containing saved model files (e.g., tensflow.joblib)
├── requirements.txt # List of required Python libraries
├── README.md # This file (project description and setup guide)
├── train-data.csv # Dataset used for training the model
├── test-data.csv # Dataset used for testing the model
└── utils # Utility functions for data preprocessing
```
---
## 📊 **Model Evaluation**
After training the model, we evaluate its performance using the following metrics:
- **Mean Absolute Error (MAE)**: Measures the average magnitude of errors in the predictions.
- **Mean Squared Error (MSE)**: Measures the average squared difference between predicted and actual values.
- **Root Mean Squared Error (RMSE)**: Square root of MSE, a more interpretable version.
- **R² Score**: Indicates how well the model explains the variance in the target data (Car Sale Price).
---
## 🧑💻 **Contributing**
We welcome contributions! Feel free to:
- **Fork** the repository.
- **Make changes** or improvements to the code.
- **Submit a pull request** with your changes.