Boston House Price Prediction using Lasso Regression

This project involves the prediction of house prices in Boston using Lasso Regression in Jupyter Notebook. The dataset contains features such as average number of rooms per dwelling, crime rate, and more. Through this analysis, we aim to build a regression model that accurately predicts house prices based on the given input features.

Dataset

The Boston housing dataset used for this analysis includes various features related to houses in Boston, such as crime rate, average number of rooms per dwelling, nitric oxides concentration, and the target variable: house price.

Prerequisites

Before running the code, make sure you have the following dependencies installed:

• Python (3.x)
• Jupyter Notebook
• Pandas
• NumPy
• Matplotlib
• scikit-learn

Getting Started

To get started, follow the steps below:

1. Clone the repository:

git clone https://github.com/shaadclt/Boston-House-Price-Prediction-LassoRegression.git

2. Change into the project directory:

cd Boston-House-Price-Prediction-LassoRegression

3. Install the required dependencies:

4. Run Jupyter Notebook:

jupyter notebook

5. Open the Boston House Price Prediction.ipynb notebook in Jupyter.

6. Run the notebook cells to load the dataset, perform data preprocessing, train the Lasso Regression model, and evaluate its performance.

Analysis Overview

The notebook provides a step-by-step guide to predict house prices in Boston using Lasso Regression. The analysis includes the following tasks:

• Loading and understanding the dataset
• Data cleaning and preprocessing
• Feature selection and transformation
• Splitting the dataset into training and testing sets
• Training the Lasso Regression model
• Evaluating the model's performance using metrics such as mean squared error and R-squared score
• Making predictions on new data points

Results and Insights

After training the model and evaluating its performance, you will gain insights into how well the Lasso Regression model predicts house prices based on the given input features. The notebook includes performance metrics and visualizations to assess the accuracy of the model. Feel free to refer to the notebook for detailed results and interpretations.

Customization

You can customize the analysis to suit your specific requirements. For example, you can experiment with different feature engineering techniques, try different regression algorithms, or incorporate additional features from the dataset to improve the model's accuracy.

License

This project is licensed under the MIT License. See the LICENSE file for more information.

Acknowledgments

• This analysis is inspired by the need to predict house prices in Boston, which can be useful for real estate agents, buyers, and sellers in making informed decisions.

Contributing

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