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🚜 Bulldozer Price Prediction

This project focuses on predicting the auction prices of bulldozers using historical data. It's designed to help equipment sellers and buyers estimate pricing trends more effectively, providing insights into how different factors affect price outcomes.

📊 Project Overview

The goal of this project is to develop a machine learning model that predicts the sale price of bulldozers at auctions based on historical data and other related factors.

🧠 Machine Learning Workflow

The general process followed in this project includes:

  1. Data Preprocessing: Cleaning and preparing the dataset for modeling.
  2. Feature Engineering: Creating new features and refining the existing ones.
  3. Model Training: Applying various machine learning algorithms to train the model.
  4. Evaluation: Testing the model on unseen data and evaluating its performance.

📂 Dataset

The dataset used in this project consists of historical auction prices of bulldozers, along with additional features such as year, equipment type, and other specifications. The dataset is split into:

  • Training set: Used to train the model.
  • Test set: Used to evaluate the model's performance.

🚀 Getting Started

To get started with this project, you'll need to set up your environment with the necessary packages and dependencies.

  1. Clone this repository:

    gh repo clone https://github.com/maheera421/Bulldozer-Price-Prediction-Model.git

  2. Install the required packages listed in the requirements.txt file:

    pip install -r requirements.txt

⚙️ Requirements

This project uses the following main libraries:

  • pandas: For data manipulation.
  • numpy: For numerical computations.
  • scikit-learn: For model building and evaluation.
  • matplotlib and seaborn: For data visualization.

For a full list of packages, refer to the requirements.txt file.

📈 Model Performance

The performance of the model is evaluated using metrics like:

  • Mean Absolute Error (MAE): Measures the average magnitude of the errors.
  • R-squared (R²): Indicates the proportion of variance in the dependent variable explained by the model.

✨ Features

  • Year: The year the bulldozer was manufactured.
  • Usage: Hours of usage for the bulldozer.
  • Model: The specific bulldozer model.
  • And many other features that affect the price prediction.