This repository contains a machine learning model built using the Random Forest Regressor algorithm to predict the prices of bulldozers. The model is trained on the Bulldozer Price Dataset, which consists of various features related to bulldozer attributes and historical sale prices.
The Bulldozer Price Dataset is a collection of records containing information about bulldozers, such as its age, usage, size, and configuration. It also includes the sale price of each bulldozer. The dataset is used for training and evaluating the machine learning model.
The Random Forest Regressor algorithm is employed to build the machine learning model for predicting bulldozer prices. The Random Forest algorithm is an ensemble learning method that combines multiple decision trees to make predictions. It handles non-linear relationships well and is robust against overfitting.
The model is trained using the features from the Bulldozer Price Dataset and their corresponding sale prices. During the training process, the Random Forest Regressor algorithm learns patterns and relationships in the data to make accurate predictions.
To use the trained model for predicting bulldozer prices, follow these steps:
- Ensure that Python and the required libraries are installed.
- Prepare input data containing the relevant features of a bulldozer.
- Use the model's predict function to obtain the predicted price for the given input data.
The performance of the model is evaluated using various evaluation metrics, such as root mean squared error (RMSE). These metric help assess the accuracy and precision of the model's predictions.