You may need to edit the code to correctly load and read the dataset based on your file structure and data source. Ensure the dataset is accessible and properly referenced in the code before running the project.
This project demonstrates a complete workflow for building a machine learning regression model to predict bulldozer prices. It includes data preprocessing, feature engineering, model training, and evaluation.
This project is implemented in Python and utilizes various data science and machine learning tools for an end-to-end solution.
- Data Cleaning: Handling missing values and inconsistent data.
- Feature Engineering: Creating meaningful features to improve model performance.
- Model Training: Using regression algorithms to predict bulldozer prices.
- Evaluation: Assessing model accuracy and performance using relevant metrics.
Before running the project, ensure you have the following Python packages installed:
pandasnumpymatplotlibseabornscikit-learnjupyter
You can install the required packages using:
pip install pandas numpy matplotlib seaborn scikit-learn jupyter- Clone the repository:
git clone https://github.com/your-username/Bulldozer-Price-Prediction.git cd bulldozer-price-regression - Edit the code to ensure the dataset is correctly loaded.
- Launch Jupyter Notebook:
jupyter notebook end-to-end-bulldozer-price-regression.ipynb
- Run the cells sequentially to reproduce the analysis and model training.
The project provides insights into bulldozer price trends and evaluates the performance of the regression model. Key outputs include:
- Visualization of price trends and feature relationships.
- Model performance metrics such as RMSE or MAE.
The dataset used in this project contains historical bulldozer sales data. Ensure you have the dataset loaded correctly for successful execution.
Contributions are welcome! Feel free to fork this repository, submit pull requests, or report issues.