🏡 Real Estate Price Prediction Model

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📄 Project Description

This project builds a state-of-the-art machine learning model for predicting real estate prices. It leverages advanced data preprocessing, feature engineering, and visualization techniques to optimize for accuracy and interpretability.

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✨ Features

• Data Analysis and Visualization:
  • Scatter plots for price trends by property size, with regression lines and median annotations.
  • Enhanced data visualization with Seaborn and Matplotlib for outlier detection and regional trend analysis.
• Comprehensive Data Cleaning:
  • Cleans and preprocesses the dataset, including handling missing values, redundant columns, and outliers.
• Feature Engineering:
  • Adds computed metrics like price_per_sqft for standardized comparisons.
  • Clusters less frequent locations into broader categories for generalization.
• Interactive Dashboard:
  • Interactive visualizations with Plotly and a Streamlit dashboard for dynamic exploration.

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🛠 Data Preprocessing

1. Loading the Dataset: Uses pandas to load and inspect the dataset.
2. Cleaning: Drops redundant columns (like area_type, society, etc.) and handles missing values.
3. Feature Engineering:
   • Converts the total_sqft feature to a numeric format.
   • Adds price_per_sqft as a calculated metric.
4. Outlier Removal:
   • Filters out extreme outliers using IQR and z-score methods for improved model performance.

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📈 Modeling

Advanced Techniques

1. Algorithms Used:
   • Linear Regression,Lasso, Decision Trees,GridSearchCV and Random Forest Regressors.
2. Cross-Validation and Evaluation:
   • Uses cross_val_score for model evaluation and selects the best-performing algorithms.

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📋 Requirements

To run this project, you need the following libraries:

• Python 3.9+
• Pandas 1.3+
• NumPy 1.21+
• Scikit-learn 0.24+
• Matplotlib 3.4+
• Seaborn 0.11+
• Plotly 5.0+ (for interactive plots)
• Streamlit (for dashboard)

Install dependencies using: bash pip install -r requirements.txt

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🚀 Usage

1. Clone the repository: bash git clone https://github.com/your-repo/real-estate-price-prediction.git

2. Navigate to the project directory: bash cd real-estate-price-prediction

3. Run the main analysis script: bash python main.py

4. (Optional) Start the interactive dashboard: bash streamlit run dashboard.py

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mermaid graph TD A[Load Dataset] --> B{Check Missing Values} B -- Yes --> C[Drop Missing Values] B -- No --> D[Proceed with Data] C --> E[Feature Engineering] D --> E E --> F[Convert Non-Numeric Features] F --> G{Check Outliers} G -- Yes --> H[Remove Outliers] G -- No --> I[Feature Scaling] H --> I I --> J[Split Dataset] J --> K[Model Selection] K --> L[Hyperparameter Tuning] L --> M[Model Evaluation]

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## 📊 Interactive Visualizations
### Plotly and Streamlit Dashboard
This project includes a Streamlit dashboard for interactive visualizations:
1. *Geospatial Heatmap*: View price distributions by neighborhood.
2. *Price Distribution*: Histograms and density plots showing the spread of prices.
3. *Filterable Dashboard*: Adjust location, price range, and property size filters to gain insights.

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## 🚧 Future Work
- Integrate *Deep Learning* models for higher accuracy on complex datasets.
- Enable real-time data updates for continuous model improvement.
- Expand dashboard features to include predictive insights based on user-defined criteria.

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## 📝 License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more information.