🚀 Streamlit Deployment:
https://real-estate-intelligence-platform-1135.streamlit.app/
The Real Estate Intelligence Platform is an end-to-end machine learning and analytics system focused on Gurgaon residential real estate intelligence.
The platform combines:
- Large-scale property data collection
- Advanced feature engineering workflows
- Machine learning price prediction
- Recommendation systems
- Interactive analytics dashboards
- Geographic market intelligence
This project represents a complete applied ML engineering workflow — from raw data acquisition to deployable analytics applications.
The platform was designed to solve three major real estate intelligence problems:
| Module | Purpose |
|---|---|
| 💰 Price Prediction | Predict residential property prices |
| 📊 Market Analytics | Analyze market trends and sector intelligence |
| 🏢 Apartment Recommendation | Recommend similar apartments using similarity modeling |
Web Scraping
↓
Data Cleaning & Preprocessing
↓
Feature Engineering
↓
Outlier Treatment
↓
Missing Value Imputation
↓
Feature Selection
↓
Model Training & Evaluation
↓
Serialized Inference Pipeline
↓
Streamlit Analytics Deployment
↓
Recommendation Engine
- Predict Gurgaon property prices
- Trained regression pipeline
- Serialized sklearn inference workflow
- Engineered real estate features
Interactive dashboards for:
- Sector-level pricing analysis
- BHK distribution analysis
- Area vs price trends
- Geographic market analysis
- Luxury segment visualization
- Feature-driven insights
Recommendation workflow powered by:
- Cosine similarity matrices
- Radius-based recommendation logic
- Geographic distance calculations
- Feature similarity scoring
| Technology | Usage |
|---|---|
| Scikit-Learn | ML Pipeline |
| XGBoost | Regression Modeling |
| SHAP | Model Explainability |
| SciPy | Scientific Computing |
| category-encoders | Feature Encoding |
| Technology | Usage |
|---|---|
| Pandas | Data Processing |
| NumPy | Numerical Operations |
| Technology | Usage |
|---|---|
| Plotly | Interactive Visualizations |
| Matplotlib | Static Visualization |
| Seaborn | Statistical Analysis |
| WordCloud | Text Visualization |
| ydata-profiling | Automated EDA |
| Technology | Usage |
|---|---|
| Streamlit | Interactive Web Application |
| Technology | Usage |
|---|---|
| BeautifulSoup4 | Web Scraping |
real-estate-intelligence-platform/
├── 000_web_scraping/
├── 002_data_preprocessing/
├── 004_feature_engineering/
├── 006_EDA/
├── 007_outlier_detection_and_removal/
├── 009_missing_value_imputation/
├── 011_feature_selection/
├── 013_baseline_model/
├── 014_model_selection/
├── 015_selected_model_and_preprocessor/
├── 016_real_estate_website/
├── 017_extras_for_analytics_module/
├── 018_recommender_system/
└── 019_insight_module/
Predict residential property prices using the trained inference pipeline.
Interactive visualizations for:
- Sector pricing
- BHK distribution
- Luxury category analysis
- Geographic insights
- Market segmentation
Apartment recommendation workflow using:
- Similarity matrices
- Radius-based filtering
- Location intelligence
- Apartment similarity scoring
git clone <your-repository-url>
cd real-estate-intelligence-platformpython -m venv .venv
.\.venv\Scripts\Activate.ps1python3 -m venv .venv
source .venv/bin/activatepip install -r requirements.txtstreamlit run 016_real_estate_website/home.py- End-to-end ML workflow ownership
- Real-world real estate dataset engineering
- Large-scale feature engineering pipeline
- Serialized sklearn inference architecture
- Interactive analytics deployment
- Recommendation system integration
- Geographic intelligence workflows
- Modular ML experimentation pipeline
- Production-style deployment workflow
The repository contains dedicated workflow stages for:
- Web scraping
- Data preprocessing
- Feature engineering
- EDA
- Outlier handling
- Missing value imputation
- Feature selection
- Model experimentation
- Model selection
- Recommendation systems
- Analytics deployment
Current system architecture is:
- Notebook-driven
- Workflow modularized
- Experimentation-oriented
- Deployment-capable
- Analytics-focused
The project is fully functional for:
- local execution
- ML experimentation
- Streamlit deployment
- portfolio demonstration
Current constraints include:
- Notebook-centric workflow
- No REST API serving layer
- No centralized orchestration
- No CI/CD integration
- No automated retraining
- No experiment tracking
- No Docker deployment
- No cloud-native ML pipeline
Future improvements planned:
- FastAPI inference service
- Docker containerization
- CI/CD integration
- MLflow experiment tracking
- Automated retraining pipeline
- Modular Python package architecture
- Cloud deployment workflows
- MLOps orchestration
- Kubernetes deployment
- Feature store integration
Add application screenshots here for stronger recruiter impact:
- ML Engineering
- MLOps
- Applied Machine Learning
- Cloud AI Systems
- AI Infrastructure Engineering
This project demonstrates:
- End-to-end ML system development
- Applied regression modeling
- Recommendation system engineering
- Data engineering workflows
- Interactive analytics deployment
- Production-oriented ML thinking
- Real-world dataset handling
- Deployment and inference workflows
This project is intended for educational, research, and portfolio purposes.
If you found this project valuable, consider giving it a ⭐ on GitHub.