This project aims to strategically place Aadhar centres by leveraging geospatial data and population demographics through the power of machine learning and predictive modeling. Aadhar centres, vital for citizen services, can be optimized in their placement by analyzing geographic data and population distribution patterns.

The primary goal of this project is to:

• Optimize Placement: Identify strategic locations for Aadhar centres based on population density, geographical accessibility, and demographic factors.
• Utilize Machine Learning: Employ predictive modeling tools to forecast areas with higher demand or potential usage for Aadhar services.
• Improve Citizen Access: Enhance accessibility for citizens to avail Aadhar services by intelligent placement strategies.

• Geospatial Data Analysis: Utilize geospatial data to understand geographical patterns and distribution.
• Population Demographics: Analyze population data to identify areas with higher demand or potential usage for Aadhar services.
• Machine Learning Modeling: Apply predictive modeling techniques to forecast optimal centre placements.
• Interactive Visualization: Display analysis results and predictive models through interactive maps and visualizations.

• Python: Core language for data analysis, machine learning, and geospatial computations.
• GeoPandas: For handling geospatial data and performing spatial operations.
• Pandas, NumPy: Data manipulation and analysis.
• Scikit-learn: Implementing machine learning algorithms.
• Matplotlib, Seaborn: Data visualization.
• Jupyter Notebooks: Interactive development and visualization environment.
• Flask/Streamlit: Deploying Web App.
• QGIS: GIS Analysis

• Working on modelling different features like population, Land - use, road networks into machine learning algorithms
• Deciding to create a unified dataset that takes in all feathures for corresponding points of India
• Deploying User-Friendly Web APP

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