🏠 House Price Prediction App

An end-to-end Machine Learning web application that predicts house prices based on property features using Python, Scikit-learn, and Streamlit.

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📌 Project Overview

This project demonstrates a complete Machine Learning workflow for predicting house prices using historical housing data. It covers data preprocessing, exploratory data analysis (EDA), model training, evaluation, and deployment through an interactive Streamlit web application.

Users can enter property details through the web interface and receive an estimated house price instantly.

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🎯 Objectives

• Perform exploratory data analysis on housing data.
• Preprocess data for machine learning.
• Train and evaluate regression models.
• Deploy the trained model using Streamlit.
• Demonstrate an end-to-end Machine Learning project.

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📂 Dataset

The dataset includes various housing features such as:

• Number of Bedrooms
• Number of Bathrooms
• Living Area
• Lot Area
• Number of Floors
• Year Built
• Parking Availability
• Additional property-related features

Note: Replace this section with your actual dataset source and feature names if they differ.

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🛠️ Tech Stack

Programming Language

• Python

Libraries

• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Plotly

Deployment

• Streamlit

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🔄 Machine Learning Workflow

• Data Collection
• Data Cleaning
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Data Preprocessing
• Model Training
• Model Evaluation
• Model Serialization
• Streamlit Deployment

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📈 Model Evaluation

The model was evaluated using regression metrics such as:

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• R² Score

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📁 Project Structure

house-price-prediction-app/
│
├── House_Price_Prediction.ipynb
├── app.py
├── model.pkl
├── requirements.txt
├── README.md
├── LICENSE

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🚀 Getting Started

Clone the repository

git clone https://github.com/Samama225/house-price-prediction-app.git

Navigate to the project directory

cd house-price-prediction-app

Install dependencies

pip install -r requirements.txt

Launch the Streamlit application

streamlit run app.py

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📌 Future Improvements

• Compare multiple regression algorithms.
• Perform hyperparameter tuning.
• Improve feature engineering.
• Deploy the application to the cloud.
• Add model explainability using SHAP.
• Convert the notebook workflow into a modular Python package.

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👨‍💻 Author

Samama Shabbir

• GitHub: https://github.com/Samama225
• LinkedIn: https://www.linkedin.com/in/samama-shabbir-889a9b252/
• Email: samamashabbir125@gmail.com

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⭐ Support

If you found this project helpful, consider giving it a ⭐ on GitHub.