This machine learning project focuses on predicting house prices using the Boston Housing dataset. The goal is to develop a model that accurately estimates the prices of houses based on various features. The project involves data preprocessing, exploratory data analysis, feature engineering, model selection, hyperparameter tuning, and evaluation.

• Project Description
• Dataset
• Usage
• Results
• Contributing

The project involves building a regression model to predict house prices using the Boston Housing dataset. It explores features like crime rate, average number of rooms, accessibility to highways, and more to create an accurate prediction model. The codebase includes data preprocessing steps, visualization of data distributions, and training/testing the model.

The Boston Housing dataset contains 506 instances with 13 features each. The dataset is divided into training and testing sets. Features include attributes like per capita crime rate, the average number of rooms per dwelling, and others that influence house prices. The dataset is loaded using scikit-learn's load_boston function.

1. Explore the Jupyter Notebook Boston_House_price_prediction_ML_model.ipynb to see the step-by-step process.
2. Run the notebook to preprocess data, visualize feature distributions, and train the machine learning model.

The trained model achieves a strong performance with an R-squared score of 71%. It accurately predicts house prices based on the provided features, providing insights for potential buyers and sellers in the real estate market.

Contributions are welcome! If you find any issues or have suggestions for improvement, please feel free to open an issue or pull request.