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Beginner Machine Learning Project: Step-by-Step Guide to Predicting London House Prices
- The article provides a step-by-step guide on predicting London house prices using machine learning.
- The dataset used in the project is sourced from Kaggle, and the notebook is available on both Kaggle and GitHub.
- The author recommends following along with the notebook for the complete code.
- The three main steps involve dataset inspection, conversion to numpy, splitting into train and test sets, and normalization of data.
- Data exploration is crucial, including visualizing features and conducting correlation analysis.
- Standard machine learning models are deemed sufficient for the relatively linear dataset; deep learning is not considered necessary.
- Linear Regression initially showed poor results, leading to experimentation with decision tree models like Random Forest.
- Gradient Boosting was found to be the most effective model, providing a solid score that was hard to beat.
- A PyTorch neural network was also implemented, but Gradient Boosting remained the preferred choice due to its performance.
- The article emphasizes the importance of data normalization, understanding model evaluation metrics like r2 score, and optimizing hyperparameters for model performance.
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