XGBoost Regression Model for Boston House Pricing Prediction

Overview

This repository contains the code and resources for building a prediction model using XGBoost regression combined with RandomizedSearchCV for hyperparameter tuning. The model is trained and evaluated on the Boston House Pricing dataset available on Kaggle.

Dataset

The Boston House Pricing dataset is available on Kaggle: Boston Housing Prices dataset

• Dataset Description: The dataset includes various features related to housing in Boston and the target variable is the median value of owner-occupied homes in $1000s.
• Dataset Structure:
  • housing.csv: CSV file containing the measurements and corresponding labels.

Model Training and Hyperparameter Tuning

The XGBoost regression model is fine-tuned using RandomizedSearchCV for hyperparameter optimization. The search space includes parameters such as learning rate, max depth, number of estimators, subsample, colsample_bytree, and gamma.

Evaluation

The model's performance is evaluated using mean squared error on the test set. The evaluation results and best hyperparameters are printed in the notebook.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or create a pull request.