The Boston House Price Prediction refers to a machine learning project that aims to estimate the value of houses in the Boston area using various predictive models. This project typically involves analyzing a dataset containing features such as the number of rooms, crime rate, property tax rate, and others that influence house prices. The goal is to create a regression model that can accurately predict the price of a house based on these features.
Here’s a brief overview of what project entail:
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Data Collection: We’ll send a request to the specified URL to retrieve the Boston housing dataset.
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Feature Selection: Identifying the most relevant features that have a significant impact on house prices.
The heatmap of correlation between features
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Model Building: Using machine learning algorithms XGBoost Regressor to build predictive models.
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Model Evaluation: Assessing the performance of the models using metrics like Accuracy, Mean Squared Error (MSE), or Mean Absolute Error (MAE).
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Prediction: Using the trained model to predict house prices given a set of features.
| Prices vs Predicted Prices | Predicted vs Residuals |
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