This project focuses on predicting bulldozer prices using machine learning techniques. The goal is to build a model that can accurately estimate the selling price of bulldozers based on various features provided in the dataset.
The project involves data preprocessing, feature engineering, and building a predictive model to estimate bulldozer prices. The dataset includes information about bulldozer features such as model, year of manufacture, usage, and other relevant attributes.
- Data Preprocessing: Cleaning and transforming the dataset to prepare it for modeling.
- Feature Engineering: Creating and selecting features that improve model performance.
- Model Building: Implementing and training machine learning algorithms to predict bulldozer prices.
- Model Evaluation: Assessing model performance using metrics such as RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error).
Ensure you have the following installed:
- Python 3.7+
- Required Python libraries (listed in requirements.txt)
- Clone the repository:
bash
git clone https://github.com/ceodaniyal/bulldozer-price-prediction-project.git
cd bulldozer-price-prediction-project
- Install the required libraries:
bash
pip install -r requirements.txt
- Run the Jupyter Notebook:
bash
jupyter notebook Bulldozer_Price_Prediction.ipynb
Open the Jupyter Notebook and follow the steps to preprocess the data, build the model, and evaluate its performance. Modify the notebook to test different models or refine the feature engineering process.
The model achieves an accuracy of XX% (replace with actual value) in predicting bulldozer prices. Key metrics include RMSE and MAE.
This project is licensed under the MIT License.