Thinklytics

A Data analysis and visualization tool for real estate market insights

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ThinkLytics - Property Market Analysis

A comprehensive data analysis and visualization tool for real estate market insights, built with Python and Streamlit.

Features

  • Interactive Data Visualization: Dynamic charts and graphs for market trends
  • Statistical Analysis: Detailed statistical insights including regression analysis
  • Market Insights: Automated market analysis and predictions
  • Advanced Filtering: Multi-parameter filtering system for data exploration
  • Personalized Rent Prediction: Instantly see your most likely monthly rent based on property size (Marla), bedrooms, and location, using a data-driven model.
    Look for the "Your Most Likely Monthly Rent" section in the dashboard!
  • Dark Theme: Responsive UI with theme support

Installation

  1. Clone the repository:

    git clone https://github.com/Mfahad159/Thinklytics.git
cd Thinklytics

  2. Create a virtual environment (recommended):

    python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

  3. Install required packages:

    pip install -r requirements.txt

Project Structure

Thinklytics/
├── main/
│   ├── analysis.py      # Statistical analysis functions
│   ├── utils.py         # Utility functions
│   ├── app.py          # Main Streamlit application
│   ├── summary.py      # Market insights and predictions
│   └── statistical_analysis.py  # Advanced statistical methods
├── data/
│   └── zameen_rentals_data.csv  # Dataset
├── requirements.txt     # Project dependencies
├── README.md           # Project documentation
└── .gitignore          # Git ignore rules

Usage

  1. Ensure you have the dataset in the correct location (data/zameen_rentals_data.csv)
  2. Run the Streamlit application: bash streamlit run main/app.py

Features in Detail

Market Trends

  • Price distribution analysis
  • Location-based property distribution
  • Bedroom count analysis
  • Price trends across locations

Advanced Analysis

  • Correlation heatmaps
  • Regression analysis
  • Feature importance visualization

Market Insights

  • Automated market summaries
  • Price predictions
  • Key market metrics

Statistical Analysis

  • Descriptive statistics
  • Confidence intervals
  • Distribution analysis
  • Multiple regression modeling

Personalized Rent Prediction

  • Get an instant, data-driven estimate of your most likely monthly rent
  • Adjust Marla, bedrooms, and location filters to see updated predictions
  • Interactive, collapsible section with themed styling for clarity and focus

Dependencies

  • Python 3.8+
  • Streamlit
  • Pandas
  • NumPy
  • Plotly
  • Seaborn
  • Matplotlib
  • SciPy
  • Statsmodels

Contributing

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

Acknowledgments

  • Data source: Zameen.com
  • Built with Streamlit
  • Statistical analysis powered by SciPy and Statsmodels

Contributors

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