Boston House Price Prediction

This project implements a linear regression model to predict house prices in Boston based on various features. It includes a Jupyter notebook for data analysis and model training, as well as a Streamlit app for interactive predictions.

Key Components

• House Prediction - Linear Regression.ipynb: Jupyter notebook containing data analysis, model training, and evaluation
• app.py: Streamlit app for interactive house price predictions
• BostonHousing.csv: Dataset with Boston housing data
• requirements.txt: Required Python packages
• setup.py: Package setup file
• Procfile and setup.sh: Files for Heroku deployment

Functionality

The notebook performs the following steps:

1. Loads and explores the Boston housing dataset
2. Preprocesses the data
3. Splits data into training and test sets
4. Trains a linear regression model
5. Evaluates model performance
6. Visualizes results

The Streamlit app allows users to:

• Input housing features via sliders
• Get a predicted house price based on those inputs

Usage

To run the Streamlit app locally:

pip install -r requirements.txt
streamlit run app.py

The app is also deployed on Heroku for online access.

Model Performance

The linear regression model achieves:

• R-squared: 0.669
• Mean Squared Error: 24.29

Visualizations in the notebook show the model's predictive performance.