This project is a collaborative notebook that uses machine learning techniques to predict the price of bulldozers. The notebook is built using Python and the popular machine learning library, Scikit-learn. It is a great resource for those interested in machine learning and predictive analytics for the construction industry, as it demonstrates how to build a machine learning model to predict bulldozer prices using real-world data.
To run this project, you will need to have access to Google Colab, a free Jupyter notebook environment that allows you to write and run Python code in the cloud. You can access it by signing in with your Google account here.
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To start using the notebook, follow these steps:
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Open the Google Colab notebook by clicking on the
bulldozer_price_prediction.ipynbfile in the repository. -
Click the 'Open in Colab' button to open the notebook in Google Colab.
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Follow the instructions in the notebook to load the dataset, preprocess the data, and build a machine learning model to predict bulldozer prices.
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Run the cells in the notebook to train the model and evaluate its performance on a test set of data.
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Experiment with different machine learning algorithms and hyperparameters to improve the model's performance.
This project is licensed under the MIT License - see the LICENSE file for details.
This project was inspired by the challenges of predictive analytics in the construction industry and built using the knowledge gained from learning Python, Scikit-learn, and machine learning techniques.