C++ Data Analyzer

A comprehensive command-line data analysis tool built in C++ that can process CSV files and provide statistical insights.

Features

📊 Core Analytics

• Dataset Overview: Display basic information about your data
• Statistical Analysis: Calculate mean, median, standard deviation, min/max for numeric columns
• Categorical Analysis: Frequency distribution and percentage breakdowns
• Correlation Analysis: Find relationships between numeric variables

🔍 Data Exploration

• Data Preview: View first N rows of your dataset
• Search Functionality: Find specific values across all columns
• Data Filtering: Export subsets based on conditions

📈 Output Features

• Formatted Display: Clean, tabular output for easy reading
• Export Capability: Save filtered results to new CSV files
• Interactive Menu: User-friendly command-line interface

Building the Project

# Compile using the Makefile
make

# Or compile manually
g++ -std=c++17 -Wall -Wextra -O2 -o data_analyzer main.cpp

Usage

1. Run the program:

   ./data_analyzer

2. Enter your CSV filename when prompted

3. Use the interactive menu to explore your data:

   • View dataset information
   • Analyze specific columns
   • Find correlations
   • Search for specific values
   • Export filtered data

Sample Data

The project includes sample_data.csv with employee information to test the analyzer. You can use your own CSV files as well.

Example Analysis Output

=== ANALYSIS FOR: Salary ===
Count: 10
Mean: 56800.00
Median: 56500.00
Min: 45000.00
Max: 70000.00
Standard Deviation: 8234.56
Sum: 568000.00

=== CORRELATION ANALYSIS ===
Age <-> Salary: 0.743
Age <-> Years_Experience: 0.856
Salary <-> Performance_Score: 0.621

Project Impact

🎯 Technical Skills Demonstrated

• Object-Oriented Programming: Clean class design with encapsulation
• File I/O: CSV parsing and data export
• STL Usage: Vectors, maps, algorithms, and iterators
• Mathematical Computing: Statistical calculations and correlations
• Memory Management: Efficient data structures
• Error Handling: Robust input validation

🚀 Real-World Applications

• Business data analysis
• Research data processing
• Financial data insights
• Performance metrics analysis
• Quality assurance reporting

📚 Learning Outcomes

• Data structure selection for performance
• Algorithm implementation for statistics
• User interface design for CLI applications
• Code organization and maintainability
• Testing with real datasets

Extending the Project

Next Steps to Make It Even More Impactful:

1. Add data visualization (ASCII charts)
2. Implement machine learning algorithms (linear regression)
3. Support for JSON and other data formats
4. Multi-threading for large datasets
5. Database connectivity
6. Web interface using C++ web frameworks

Author

dhrumil246 - Created on 2025-05-24

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This project demonstrates practical C++ skills while solving real data analysis problems.