https://github.com/asenacak/usedcarsml

https://github.com/asenacak/usedcarsml

data-preprocessing decision-tree-regressor feature-selection gridsearchcv hyperband hyperparameter-tuning imputation-methods keras keras-tuner linear-regression machine-learning neural-networks normalization one-hot-encoding price-prediction random-forest-regression selectkbest tensorflow used-cars-price-prediction xgboost-regression

Last synced: about 2 months ago
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• Host: GitHub
• URL: https://github.com/asenacak/usedcarsml
• Owner: asenacak
• License: mit
• Created: 2024-04-24T15:09:29.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-23T18:09:16.000Z (6 months ago)
• Last Synced: 2025-01-18T07:44:39.559Z (4 months ago)
• Topics: data-preprocessing, decision-tree-regressor, feature-selection, gridsearchcv, hyperband, hyperparameter-tuning, imputation-methods, keras, keras-tuner, linear-regression, machine-learning, neural-networks, normalization, one-hot-encoding, price-prediction, random-forest-regression, selectkbest, tensorflow, used-cars-price-prediction, xgboost-regression
• Language: Jupyter Notebook
• Homepage:
• Size: 229 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Used Cars Price Prediction

![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)

![Jupyter Notebook](https://img.shields.io/badge/jupyter-%23FA0F00.svg?style=for-the-badge&logo=jupyter&logoColor=white)

![TensorFlow](https://img.shields.io/badge/TensorFlow-%23FF6F00.svg?style=for-the-badge&logo=TensorFlow&logoColor=white)

![Keras](https://img.shields.io/badge/Keras-D00000?style=for-the-badge&logo=Keras&logoColor=white)

![scikit-learn](https://img.shields.io/badge/scikit--learn-F7931E?style=for-the-badge&logo=scikit-learn&logoColor=white)

![XGBoost](https://img.shields.io/badge/XGBoost-ADD8E6?style=for-the-badge&logo=XGBoost&logoColor=white)

![Pandas](https://img.shields.io/badge/pandas-%23150458.svg?style=for-the-badge&logo=pandas&logoColor=white)

![Matplotlib](https://img.shields.io/badge/Matplotlib-%23ffffff.svg?style=for-the-badge&logo=Matplotlib&logoColor=black)

This project involves predicting the prices of used cars using various machine learning models. The dataset used for this analysis comes from the [US Used Cars dataset (3 million)](https://www.kaggle.com/datasets/ananaymital/us-used-cars-dataset), containing detailed information about used cars in the United States.

## Project Overview

The goal of this project is to build and compare multiple regression models to predict the prices of used cars. The models included in this analysis are:

- **Linear Regression**

- **Decision Trees**

- **Random Forest**

- **eXtreme Gradient Boosting (XGBoost)**

- **Deep Neural Networks (DNN)**

The performance of each model is evaluated using **Root Mean Squared Error (RMSE)** as the key metric. Additionally, techniques such as **GridSearchCV** and **Hyperband (Keras Tuner)** are used to fine-tune model parameters and achieve optimal performance.

## Data Source

The dataset used in this project is publicly available on Kaggle:

- **[US Used Cars dataset (3 million)](https://www.kaggle.com/datasets/ananaymital/us-used-cars-dataset)**

## Data Cleaning and Preprocessing

1. **Handling Placeholder Characters**: The dataset contains placeholder characters represented as `--`, which are cleaned before further processing.

   

2. **Missing Values**: A comprehensive approach is used to deal with missing data:

   - Removal of missing entries in certain columns.

   - Mean imputation, mode imputation, and multiple imputation techniques are applied where appropriate.

3. **Categorical Encoding**: One-hot encoding is applied to convert categorical variables into a numerical format that can be used by machine learning models.

4. **Normalization**: Numerical columns are standardized to ensure consistent scaling across features.

5. **Feature Selection**: The **SelectKBest** method is used to identify the most informative features for predictive modeling, enhancing model performance.

## Model Evaluation

After preprocessing, five regression models are trained and evaluated using **RMSE** as the key metric. The results are as follows:

- **Random Forest**: Best performance with an RMSE of **0.00265**.

- **XGBoost**: Second-best performance with an RMSE of **0.00278**.

- **Decision Tree**: RMSE of **0.00282**, closely following XGBoost.

- **Deep Neural Network (DNN)**: RMSE of **0.00283**, slightly higher than Decision Tree.

- **Linear Regression**: RMSE of **0.00393**, significantly worse than the other models.

## Model Tuning

- **GridSearchCV**: Used for hyperparameter tuning of the XGBoost, and Decision Tree models.

- **Keras Tuner (Hyperband)**: Applied for optimizing the Deep Neural Network model's parameters.

## Conclusion

Among the five models tested, **Random Forest** outperformed the others, achieving the lowest RMSE, indicating its superior ability to predict used car prices. **XGBoost**, **Decision Tree**, and **DNN** performed similarly, while **Linear Regression** lagged behind in terms of predictive accuracy.

## How to Run the Project

1. Clone this repository.

   ```bash

   git clone https://github.com/asenacak/UsedCarsML.git

   ```

2. Download the dataset from the following link: [US Used Cars dataset (3 million)](https://www.kaggle.com/datasets/ananaymital/us-used-cars-dataset).

3. Install the necessary dependencies.

4. Run the Jupyter notebooks:

   * Used_Cars_Data_Cleaning.ipynb: Data preprocessing and cleaning.

   * models_usedcars.ipynb: Model building, evaluation, and hyperparameter tuning.

## Dependencies

* Python 3

* Jupyter Notebook

* pandas

* scikit-learn

* XGBoost

* TensorFlow/Keras

* Keras Tuner

* matplotlib

## License

This project is licensed under the MIT License.