This repository contains a comprehensive dataset of used cars with various attributes, providing insights into their characteristics and pricing. The dataset can be used for analytical and machine learning projects, such as predictive modeling, trend analysis, and visualization.
The dataset includes the following attributes:
| Attribute | Description |
|---|---|
| Car Name | The make and model of the car. |
| Year | The year of manufacture. |
| Selling Price | The price at which the car is being sold (in currency). |
| Present Price | The current showroom price of the car (in currency). |
| Kilometers Driven | The total kilometers the car has been driven. |
| Fuel Type | Type of fuel used (e.g., Petrol, Diesel, CNG). |
| Seller Type | Whether the seller is a dealer or an individual. |
| Transmission Type | Indicates whether the car has a manual or automatic transmission. |
| Previous Owners | The number of previous owners of the car. |
├── data/ # Contains the dataset files (CSV, Excel, etc.)
├── notebooks/ # Jupyter notebooks for exploratory data analysis (EDA) and modeling
├── src/ # Source code for data preprocessing, analysis, and modeling
├── README.md # This file
└── LICENSE # License information
- Python 3.8+
- Required Python libraries:
- pandas
- numpy
- matplotlib
- seaborn
- scikit-learn
Install dependencies using:
pip install -r requirements.txt- Clone the repository:
git clone https://github.com/yourusername/used-cars-dataset.git
- Navigate to the project directory:
cd used-cars-dataset - Explore the dataset using the notebooks in the
notebooks/directory.
- Price Prediction: Build machine learning models to predict the selling price of a used car.
- Trend Analysis: Analyze trends in car prices based on brand, fuel type, or year of manufacture.
- Market Insights: Identify popular car models and attributes that affect pricing.
- Visualization: Create interactive dashboards or plots for better insights.
Contributions are welcome! If you have suggestions or improvements, please:
- Fork the repository.
- Create a new branch.
- Make your changes.
- Submit a pull request.