Bulldozer Price Prediction – Machine learning project to predict bulldozer sale prices using historical auction data.

An end-to-end machine learning project to predict the sale price of bulldozers. This repository details a full data science workflow, including data preprocessing, model training with scikit-learn pipelines, hyperparameter tuning, and model evaluation.

A Regression model that predicts the price of bulldozers based on their features.

Validation RMSLE obtained: 0.21163 which is less than the RMSLE score (0.22909) that won the Kaggle Competition.

Predicting the Sale Price of Bulldozers using Machine Learning

A bulldozer price predictions workflow.

The BullDozer-Price-Prediction 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, making it a great resource for those interested in machine learning and predictive analytics for construction industry

This predicts future price of bulldozers with the use of past prices.

End-to-end Machine Learning project predicting Bulldozer Auction Prices using Random Forest Regressor on 400K+ real-world Kaggle dataset with feature engineering, hyperparameter tuning, and model evaluation.

This is a Regression problem to predict Bulldozer price based on given features using Scikit-learn. Data is downloaded from Kaggle Dataset. Achieved RMSLE (Root Mean Squared Log Error) is on Training dataset is 0.12935673426211658