Black Money Transaction Analysis and Visualization

A Python-powered Streamlit application to analyze and visualize black money transaction trends and patterns. This project leverages data analytics to extract actionable insights from suspicious financial activities.

🚀 Features

1. Data Upload:

   • Upload datasets for analysis (.csv files).
   • Interactive preview of uploaded data.

2. Data Cleaning:

   • Handle missing values.
   • Filter out irrelevant transactions.

3. Exploratory Data Analysis (EDA):

   • Summary statistics.
   • Detailed correlation analysis.
   • Insights into transaction trends over time.

4. Visualization:

   • Heatmaps to identify patterns.
   • Bar charts, line graphs, and pie charts for clear understanding.

5. Anomaly Detection:

   • Highlight suspicious transactions.
   • Generate alerts based on defined thresholds.

6. User-Friendly Interface:

   • Streamlit app for easy access and interaction.

7. Deployment:

   • Deployed and accessible via a single URL for sharing insights with stakeholders.

🛠 Tech Stack

• Programming Language: Python
• Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
• Framework: Streamlit

📊 Sample Insights

• High-value transactions often occur during specific hours, raising red flags.
• Significant correlations between transaction frequency and unusual locations.
• Detected clusters of activity around specific dates/events.

🚀 Deployment

The application is deployed on Streamlit Cloud. Access it here:
Live Demo

🙌 Acknowledgments

Thanks to the open-source community and tools that made this project possible.