A sophisticated C++ data analysis platform for processing and analyzing World Bank Development Indicators, featuring multiple advanced data structures and algorithms for efficient data storage, retrieval, and relationship analysis between countries.
This system processes large-scale economic, social, and environmental datasets to uncover relationships between countries based on various development indicators. It supports complex queries, statistical analysis, and graph-based relationship modeling, making it ideal for economic research and policy analysis.
- Purpose: Efficient country data organization and range queries
- Implementation: Custom BST with multi-country nodes supporting deletions, insertions, and traversals
- Algorithms:
- In-order traversal for sorted data retrieval
- Recursive insertion/deletion with tree balancing
- Min/Max finding for statistical analysis
- Time Complexity: O(log n) for search, insert, delete operations
- Purpose: Country relationship modeling based on statistical thresholds
- Implementation: Unordered map-based adjacency lists with custom Edge objects
- Algorithms:
- Breadth-First Search (BFS) for pathfinding between countries
- Dynamic edge creation based on statistical comparisons
- Connectivity analysis for relationship networks
- Time Complexity: O(V + E) for BFS traversal
- Purpose: O(1) country lookup and data access
- Implementation: Custom double hashing with primary and secondary hash functions
- Features:
- Collision resolution using secondary hashing
- Dynamic load factor management
- Efficient country code to data mapping
- Time Complexity: O(1) average case for lookup, insert, delete
- Purpose: Time series data management for each country
- Implementation: Custom linked list with TimeSeries nodes
- Features: Dynamic memory management with proper cleanup
- Purpose: Efficient time series data storage with automatic resizing
- Implementation: Custom resizing algorithms for optimal memory usage
- Features: Automatic capacity management and data validation
- Mean Calculation: Efficient computation for large datasets
- Monotonic Sequence Detection: Pattern recognition in time series
- Linear Regression: Best-fit line calculation (y = mx + b)
- Threshold-based Comparisons: Statistical relationship analysis
- Breadth-First Search: Shortest path finding between countries
- Connectivity Analysis: Determining if countries are related through indicators
- Dynamic Graph Construction: Real-time edge creation based on data analysis
- Range Queries: Efficient searching within statistical ranges using BST properties
- Multi-criteria Filtering: Complex queries across multiple indicators
- Pattern Matching: Country identification based on statistical patterns
- LOAD: Import World Bank CSV datasets
- INSERT: Add new countries and time series data
- UPDATE: Modify existing data points
- DELETE: Remove countries or specific indicators
- LOOKUP: O(1) country data retrieval
- LIST: Display country information and available indicators
- RANGE: Statistical range analysis across indicators
- LIMITS: Find minimum and maximum values
- FIND: Search countries meeting specific criteria
- BUILD: Construct BST for efficient querying
- INITIALIZE: Build country relationship graph
- UPDATE_EDGES: Create connections based on statistical thresholds
- ADJACENT: Find countries with direct relationships
- PATH: Determine connectivity between any two countries
- RELATIONSHIPS: Analyze specific country pair connections
- Economic Policy Analysis: Compare countries with similar economic indicators
- Development Pattern Recognition: Identify trends across nations
- Regional Analysis: Find countries with shared characteristics
- Investment Decision Support: Analyze market relationships
- Academic Research: Large-scale comparative studies
├── InputHandler.cpp # Main program and command processing
├── Graph.h/cpp # Graph implementation with BFS algorithms
├── BST.h/cpp # Binary Search Tree with advanced operations
├── Countries.h/cpp # Hash table implementation for country management
├── Country.h/cpp # Individual country data management
├── TimeSeries.h/cpp # Statistical analysis and data processing
├── LinkedList.h/cpp # Time series data organization
├── g_node.h/cpp # Graph node implementation
├── Edge.h # Graph edge representation with operator overloading
├── BST_Node.h/cpp # BST node implementation
├── Node.h/cpp # Linked list node implementation
└── Makefile # Build configuration
| Operation | Data Structure | Time Complexity | Space Complexity |
|---|---|---|---|
| Country Lookup | Hash Table | O(1) avg | O(n) |
| Range Query | BST | O(log n + k) | O(n) |
| Path Finding | Graph (BFS) | O(V + E) | O(V) |
| Statistical Analysis | Dynamic Array | O(n) | O(n) |
| Relationship Building | Graph | O(V²) | O(V + E) |
- C++17 compatible compiler
- Make utility
make./a.outLOAD lab2_multidata.csv
INITIALIZE
UPDATE_EDGES EG.ELC.ACCS.ZS 95.0 >
ADJACENT CAN
PATH USA CHN
RELATIONSHIPS USA CAN
The system processes World Bank Development Indicators in CSV format, supporting various metrics including:
- Economic indicators (GDP, inflation, trade)
- Social indicators (education, healthcare, demographics)
- Environmental indicators (energy, emissions, resources)
- Infrastructure indicators (electricity access, internet penetration)
- Multi-paradigm Design: Object-oriented architecture with efficient memory management
- Advanced Data Structures: Custom implementations of BST, Graph, Hash Table, and Linked List
- Algorithm Optimization: BFS pathfinding, statistical analysis, and dynamic memory management
- Real-world Application: Processing large-scale economic datasets for policy analysis
- Performance Engineering: O(1) lookups, O(log n) queries, and efficient graph operations
- System Architecture: Modular design with proper abstraction and encapsulation
- Memory Management: Custom destructors and proper cleanup for all data structures
- Error Handling: Robust input validation and edge case management
- Modular Design: Clean separation of concerns across multiple classes
- Documentation: Comprehensive inline documentation and design decisions
- Testing: Extensive validation with real-world datasets
This project demonstrates advanced C++ programming skills, algorithm implementation, and real-world data analysis capabilities suitable for roles in software engineering, data science, and quantitative analysis.