House-Price-Prediction
🏡 House Price Prediction System
A full-stack machine learning solution designed to estimate house prices based on regional demographics and housing attributes. This project analyzes 5,000+ housing records to empower real estate firms, property investors, and financial analysts with predictive insights and interactive dashboards for valuation, investment, and underwriting decisions.
🚗 GitHub Project Repository
🔗 Click to view House-Price-Prediction
🧠 Project Overview
Accurate house price prediction is essential for real estate valuation, investment planning, and mortgage risk assessment. This project delivers an end-to-end analytics platform that enables:
- 🏠 Property-level price estimation
- 📊 Feature impact analysis
- 🗺️ Regional pricing visualization
- 📈 Dashboard-driven insights for stakeholders
🎯 Key Objectives
- Clean and preprocess housing and demographic data
- Engineer features for regression modeling and dashboarding
- Build predictive models to estimate house prices
- Deploy interactive dashboards for business decision-making
📁 Project Structure
| File Name | Description |
|---|---|
house_price_prediction.csv |
Raw dataset with housing and demographic info |
cleaned_house_price_prediction.csv |
Preprocessed dataset with feature engineering |
house_price_model.pkl |
Trained regression model for price prediction |
house_price.sql |
SQL queries for data extraction and filtering |
sqlconnect.py |
Python script for SQL database connection |
app.py |
Streamlit app for dashboard deployment |
house_price_prediction.ipynb |
Jupyter notebook with EDA, modeling, and insights |
house_price_prediction dashboard |
Power BI or Streamlit dashboard visualizing pricing trends |
🧹 Data Preprocessing
- Imputed missing values in
avg_income,avg_population,avg_area_house_age - Normalized continuous variables (
avg_income,avg_population,avg_area_num_rooms) - Removed outliers in
priceandavg_area_num_rooms - Verified data types and optimized memory usage
- Optional enhancement: converted
addressto geolocation features
📈 Exploratory Data Analysis
- 📊 Price distribution across income brackets and population density
- 🛏️ Impact of house age and bedroom count on pricing
- 🧠 Correlation matrix of housing features vs. price
- 🗺️ Regional pricing trends based on address clustering
🤖 Modeling Approach
- Target Variable:
price - Algorithms Used: Linear Regression, Random Forest Regressor, XGBoost Regressor
- Evaluation Metrics: MAE, RMSE, R² Score
- Top Features:
avg_income,avg_area_num_rooms,avg_population,avg_bedrooms
📊 Dashboard Overview
🔷 Power BI Dashboard
Visualizes pricing trends and feature impact:
- 🗺️ Regional price heatmaps
- 📈 Income vs. price trend analysis
- 🛏️ Bedroom and room count impact visualization
- 📊 Feature distribution and correlation plots
🟢 Streamlit App
Interactive dashboard for real-time price prediction:
- 🏠 House-level price prediction tool
- 📈 Feature importance visualization
- 📊 Dynamic filtering by region and housing attributes
🚀 Deployment
- Model serialized with
joblibashouse_price_model.pkl - Dashboard deployed via Streamlit Cloud
- SQL integration for dynamic data updates
- Git LFS used for large file management
🧠 Business Impact
- Enables accurate property valuation for buyers and sellers
- Supports real estate investment decisions with data-driven insights
- Improves pricing transparency across regions
- Enhances mortgage risk assessment and underwriting
🛠️ Tech Stack
- Python: Pandas, NumPy, Scikit-learn, Streamlit
- SQL: Data extraction and filtering
- Visualization: Power BI, Matplotlib, Seaborn, Plotly
- Deployment: Streamlit Cloud, GitHub, Git LFS
📌 Future Enhancements
- Integrate geolocation APIs for address-based clustering
- Add explainability via SHAP for feature impact
- Enable user-uploaded property data for prediction
- Expand dashboard to include rental price forecasting
👤 Author
Anesh Raj