🏡 House Price Prediction

An end-to-end machine learning project to predict house prices using regression models. Built with Python and deployed via a Flask web app to demonstrate data science, model engineering, and full-stack skills.

🔍 Project Overview: This project develops and deploys a House price prediction system using historical housing data. It involves:

• Data preprocessing & feature engineering
• Model training, evaluation & comparison
• Serialization of the model pipeline
• Web interface to input house features and predict pricing

It aims to showcase skills in machine learning, data engineering, & full-stack deployment — making it ideal work sample material for roles in Data Science, Machine Learning Engineering, or Predictive Analytics.

🛠️ Key Features & Components:

• Data Handling & Feature Engineering Clean, transform, and engineer features from raw housing data to improve model accuracy.
• Model Training & Evaluation Train multiple regression models (e.g. linear regression, tree-based) and compare performance metrics (RMSE, MAE, R²).
• Pipeline & Serialization Build a robust data + model pipeline and serialize it (e.g. using pickle or joblib) for reuse in production.
• Web Application (Flask) A user-friendly UI where users enter house attributes and get a price prediction in real time.

📂 Project Structure:

House-Price-Prediction/

├── app.py # Flask app entry point

├── House_price_prediction.ipynb # Notebook showing EDA, modeling, experiments

├── kc_house_data.csv # Raw housing dataset

├── house_price_pipeline.pkl # Serialized preprocessing + model pipeline(Model)

├── requirements.txt # Project dependencies

└── README.md # This readme

💻 Technologies & Skills Demonstrated:

Area	Tools / Libraries
Data Processing & EDA	pandas, numpy, seaborn, matplotlib
Machine Learning	scikit-learn, xgboost / random forest (if used)
Model Pipeline & Serialization	scikit-learn pipelines, joblib / pickle
Web Deployment	Flask
Version Control & Project Structure	Git & GitHub

🚀 Installation & Running: Prerequisites

• Python 3.7+
• pip (Python package installer)

Steps

1. Clone the repo:

   git clone https://github.com/UK183/House-Price-Prediction.git
   cd House-Price-Prediction
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the Flask application:

   python app.py
   

Open your browser and navigate to http://127.0.0.1:5000 to use the prediction UI.

📈 Performance & Results:

Model evaluation metrics (RMSE, MAE, R²) documented in the notebook.

Comparative performance across different algorithms shown with visualizations.

Deployed model used via API endpoint in Flask app.

You can see all results, experiments, and visualizations in House_price_prediction.ipynb.

🧠 Key Learnings & Achievements:

End-to-end experience—from raw data to live web app deployment.

Hands-on exposure to feature engineering, model tuning, and deployment challenges.

Ability to package ML pipelines for production and embed them in a user interface.

⚠️ Note: This project is for educational/demonstration purposes. Real-world implementation would require additional considerations like more extensive validation, error handling, security, and scalability.

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👤 Author

Kazi Umar
Linkedin profile: https://www.linkedin.com/in/umar-kazi18
💼 Data Analyst | ML Engineer | Data Science & AI Enthusiast | Power BI | Python | SQL