The Gurugram Real Estate Analytics Project is a comprehensive data-driven solution designed to analyze, predict, and recommend real estate properties in Gurugram. This project leverages advanced machine learning, data visualization, and optimization techniques to provide actionable insights into property prices and trends.
- Prediction: Forecasting property prices using historical data and machine learning models.
- Analytics & Visualization: Interactive dashboards showcasing market trends, insights, and price distributions.
- Recommendation: Suggesting the best properties based on user preferences and data-driven insights.
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✅ Web Scraping: Automated extraction of real estate data from multiple sources.
✅ Data Cleaning & Preprocessing: Handling missing values, outliers, and ensuring data quality.
✅ EDA (Exploratory Data Analysis): Understanding patterns, trends, and distributions using visualization techniques.
✅ Feature Engineering: Creating new features to enhance model performance.
✅ TF-IDF & Cosine Similarity: Used for property similarity recommendations.
✅ Column Transformer & Scaling: StandardScaler & OneHotEncoding for feature transformation.
✅ Pandas Profiling & Ordinal Encoding: Automating data analysis and preprocessing.
✅ K-Fold Cross-Validation: Enhancing model evaluation and validation.
✅ Feature Selection:- GradientBoostingRegressor - Permutation Importance - Lasso Regression - SHAP (SHapley Additive Explanations)
✅ Dimensionality Reduction: Implementing PCA (Principal Component Analysis).
✅ Model Selection & Training: Comparing ML models to find the best fit.
✅ Bayesian Optimization (Optuna): Hyperparameter tuning for optimal performance.
✅ Deployment with Streamlit & AWS: Interactive web app deployed on AWS.
1️⃣ Data Collection: Scraping property listings, historical prices, and location details.
2️⃣ Data Cleaning & Processing: Handling missing values, encoding categorical data, and standardizing numerical features.
3️⃣ Exploratory Data Analysis (EDA): Visualizing price trends, feature correlations, and market insights.
4️⃣ Feature Engineering & Selection: Creating new features and selecting the most impactful ones.
5️⃣ Model Training & Evaluation: Experimenting with regression models:
- Linear Regression, Random Forest, XGBoost, LightGBM
- Support Vector Regression (SVR), Logistic Regression
6️⃣ Hyperparameter Tuning: Bayesian Optimization with Optuna.
7️⃣ Feature Selection: Using GradientBoostingRegressor, Permutation Importance, Lasso, SHAP.
8️⃣ Dimensionality Reduction: PCA applied for performance improvement.
9️⃣ Deployment: Interactive Streamlit web application deployed on AWS Cloud Services.
🔹 Python: Pandas, NumPy, Scikit-learn, BeautifulSoup, Selenium
🔹 Machine Learning: XGBoost, LightGBM, Random Forest, Linear Regression, Multilayer Perceptron, SVR
🔹 Feature Engineering: TF-IDF, Cosine Similarity, Ordinal Encoding, PCA
🔹 Hyperparameter Tuning: Optuna (Bayesian Optimization)
🔹 Visualization: Matplotlib, Seaborn, Plotly
🔹 Web Framework: Streamlit (for deployment)
🔹 Data Storage: CSV, SQLite
🔹 Cloud Services: AWS (EC2, S3, Lambda)
🔹 Geospatial Analysis (Geo Maps): Property locations, pricing heatmaps, area-wise trends.
🔹 Time-Series Analysis: Animations for price changes over time.
🔹 Multi-Map Comparisons: Side-by-side comparisons of different property zones.
🔹 Correlation Heatmaps: Identifying relationships between key features and property prices.
🔹 3D Visualizations: Interactive 3D graphs for property distribution.
These visualizations enhance real estate market understanding and data-driven investment decisions.
✅ High Accuracy: Achieved a strong R² score with optimized ML models.
✅ Market Trends: Identified key factors influencing property prices in Gurugram.
✅ Optimized Recommendations: Personalized property suggestions using Cosine Similarity.
# Clone the repository
git clone https://github.com/AbhaySingh71/99acres-Real-Estate-Analytics.git
# Navigate to the project directory
cd 99acres-Real-Estate-Analytics
# Install dependencies
pip install -r requirements.txt
# Run the Streamlit app
streamlit run app.py
The project is deployed on AWS Cloud Services to ensure high availability and scalability. The following services are utilized:
🔹 EC2: Hosting the Streamlit application.
🔹 S3: Storing dataset files and model artifacts.
🔹 Lambda: Handling backend processing tasks.
Contributions are welcome! If you have suggestions or improvements, feel free to fork the repository and submit a pull request.
🚀 Empowering smarter real estate decisions with data-driven insights! 🏡📊
#DataScience #MachineLearning #RealEstateAnalytics #AWS #HappyLearning 😊