Bulldozer Auction Price Prediction with Machine LearningBulldozer Auction Price Prediction with Machine Learning
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Tobechukwu Samuel
• 18h
🏗️ Bulldozer Price Prediction — End-to-End Machine Learning Project 🚜 Overview
How do you accurately estimate the value of heavy equipment at auction?
In this project, I built a machine learning model to predict the sale price of bulldozers using historical auction data — effectively creating a data-driven “blue book” valuation system.
This project simulates a real-world ML workflow:
Handling messy, real-world data Engineering meaningful features Iterating through models and tuning Evaluating performance using proper metrics 🎯 Problem Statement
Auction prices for heavy equipment can vary significantly based on:
Machine specifications Usage and configuration Market conditions over time The objective is to build a model that can accurately predict the SalePrice, enabling:
Better pricing decisions Reduced uncertainty in auctions Data-driven valuation systems 📊 Dataset
The dataset is split into three time-based sets:
Train Set → Data up to 2011 Validation Set → Jan 2012 – April 2012 Test Set → May 2012 – Nov 2012 This structure mimics real-world forecasting, where models are trained on past data and evaluated on future data.
⚠️ Due to size limitations, the dataset is not included in this repository. 👉 Download here: https://www.kaggle.com/competitions/bluebook-for-bulldozers/data
⚙️ Machine Learning Workflow
🧹 Data Preprocessing
Converted saledate to datetime format Extracted time-based features: Year, Month, Day, Day of Week Handled missing values: Numerical → median imputation Categorical → encoded as numerical values 🧠 Feature Engineering
Created time-based features from sale date Leveraged machine attributes and configuration data Improved model performance through iterative feature refinement 🌲 Model Used
RandomForestRegressor
Why?
Handles non-linear relationships well Works great with structured/tabular data Robust to noise and missing values 📈 Results
Metric Training Validation MAE 2953.82 5951.25 RMSLE 0.1447 0.2452 R² 0.9588 0.8818 🔁 Iteration Journey (What Actually Happened)
This project wasn’t a straight line — and that’s where the real learning happened.
Stage Validation RMSLE Baseline Model 0.2936 First Tuning Attempt 0.5638 ❌ Final Optimized Model 0.2452 ✅ 💡 Key Takeaway:
Better hyperparameters don’t guarantee better performance — experimentation does.
🔧 Hyperparameter Tuning
Used RandomizedSearchCV (100 iterations) to explore the parameter space.
Best parameters:
n_estimators=40 min_samples_leaf=1 min_samples_split=14 max_features=0.5
💡 Key Insights Feature engineering had the biggest impact on performance Poor tuning can significantly degrade model accuracy Time-based splits are crucial for realistic evaluation Iteration and experimentation are core ML skills
🚀 Future Improvements Apply log transformation to improve RMSLE Experiment with LightGBM / XGBoost Build a deployment-ready app (Streamlit) Add feature importance visualization
📁 Project Structure bulldozer-price-prediction/ │ ├── notebook.ipynb ├── README.md ├── .gitignore └── requirements.txt
🧑‍💻 Author Toby Chuks GitHub: https://github.com/tobychuks01 LinkedIn: https://www.linkedin.com/in/toby-chuks-630b44217
⭐ Final Note This project reflects more than just building a model — it demonstrates the importance of iteration, experimentation, and learning from failure in machine learning.
Machine Learning EngineeringData Analysisscikit-learnpandasPythonAI Engineer
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