House Price Prediction Using Machine Learning Algorithm

This research paper presents a thorough investigation into house price prediction utilizing a Kaggle dataset. Employing advanced machine learning techniques, the study seeks to provide valuable insights and methodologies within the field of real estate prediction. The analysis is based on a Kaggle competition. The research initiates with a meticulous exploration of the dataset, encompassing feature engineering and the handling of missing data. Subsequently, a diverse set of machine learning models, such as linear regression, gradient boosting, and neural networks, is implemented. The study emphasizes thoughtful feature selection and optimization strategies to augment model performance. Significant attention is devoted to elucidating the importance of data preprocessing and the rationale behind model selection, prioritizing interpretability and accuracy. The paper also discusses challenges encountered during the predictive modelling process and corresponding solutions devised.