Built a mathematical model that could predict the price of a used car based on previous consumer data and the collection of characteristics by using the supervised machine learning techniques. This uses 3 different algorithms of Machine Learning:
- Linear Regression
- Random Forest
- Naïve Bayes
Libraries used in this project are as follows:
- numpy
- pandas
- seaborn
- matplotlib
- sklearn
You can clone this repository using command: git clone https://github.com/Jimmy5467/Second-hand-car-price-prediction.git
You can open the .ipynb file using Google Collab or Jupyter Notebook
Don't have Jupyter Notebook? Don't worry, run the following commands and you're good to go 🚀
$ pip install notebook
$ jupyter notebook
3. Execute all the cells consecutively to see the accurecy of all model and the graphical reprentaion of the data.
4. After fireing the queries, at the end of all the algorithms we will get the price of the selected car.
The least square method was used to estimate the model, and the following Minitab results were obtained.
| R - Square Value | Accuracy |
|---|---|
| Linear Regression | 83.67% |
| Naïve Bayes | 88.68% |
| Random Forest | 89.01% |
The percentage answer variance in a variable called R-square is explained by a linear model (Rsq). This means that a high R-square value indicates that the model is more suited to the data and hence produces more reliable results.
Feel free to open an issue. We are glad to help you. 💙
MIT