House Price Prediction Using Flask

Abstract

1. Introduction

This project demonstrates the predictive capabilities of a model trained on house price data using Linear Regression. The model is deployed using a Flask API, providing an interface to predict house prices based on input features.

For a detailed explanation, check out my blog post: House Price Prediction using Flask for Beginners

2. Objective

Model deployment with flask api, using Linear Regression to predict the price value. Deploy ML Models Using Flask to take your models from python to production.

This project demonstrates how Python, Flask, Machine Learning can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/MdJafirAshraf/House-price-prediction-using-flask.git
cd House-price-prediction-using-flask

pip install -r requirement.txt

python app.py

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by MdJafirAshraf and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.