This repository contains a notebook for detecting the quality of used cars based on various features. The project uses machine learning models and data analysis to predict car quality.
This project aims to develop a machine-learning model that predicts the quality of a used car based on its features. The analysis includes data cleaning, feature engineering, model building, and evaluation.
- Data Preprocessing: Data is cleaned and prepared for machine learning.
- Feature Engineering: Features are created and transformed for better model performance.
- Model Building: Various machine learning models are built and tested.
- Model Evaluation: The performance of the models is evaluated using appropriate metrics.
To run the code in this repository, you need to have the following libraries installed:
- Python 3.x
- Jovian
- OpenDatasets
- Pandas
- Scikit-learn
You can install the dependencies using the following command:
pip install jovian opendatasets pandas scikit-learn
- Clone the repository using Git:
git clone https://github.com/your-username/used-car-quality-detection.git - Navigate to the project directory:
cd Used_car_detection - Run the Jupyter notebook:
jupyter notebook used-car-quality-detection.ipynb
Follow the instructions in the notebook to execute each cell and view the outputs.
The dataset used in this project is downloaded using the opendatasets package. It contains various features related to used cars, which help in determining the quality of the cars. You can modify the dataset by downloading your own or using the one specified in the notebook.
To download the dataset, the following command is used:
import opendatasets as od
od.download('dataset-url')The models were evaluated based on accuracy, precision, recall, and other relevant metrics. The best model achieved satisfactory performance on the test set.