Interview Performance Analytics Dashboard

[vandita-yadav]

📊 Interview Performance Analytics Dashboard

💡 Proposal Overview

Hello, I'm Vandita Yadav, a contributor under GSSOC'25. I specialize in Data Science and building applications with Python, including AI/ML libraries, Pandas, Matplotlib, and Streamlit. I propose adding a standalone analytics module to enhance interview.io capabilities.

💡 Data-Driven Interview Preparation

Add a comprehensive analytics module to help users track their mock interview performance, identify patterns, and focus their preparation effectively using simple CSV-based data analysis.

🎯 Features to Implement

1. Performance Trend Analyzer

• Progress Visualization: Line charts showing score improvements over time across different interview types (Technical, HR, Behavioral, Coding)
• Category Breakdown: Pie charts and bar graphs displaying performance by question topic (Algorithms, System Design, Databases, etc.)
• Consistency Metrics: Track preparation effectiveness and identify learning plateaus

2. Weakness Identification & Study Planner

• Skill Gap Analysis: Automatically detect topics needing more practice based on historical performance
• Priority Recommendations: "Focus on Dynamic Programming - your scores are 30% lower here"
• Personalized Roadmap: Data-backed suggestions for what to practice next

📁 Simple CSV Data Structure

The system will work with clean, structured CSV data:

interview_date,interview_type,topic,score,duration_minutes,feedback_notes
2024-03-20,Technical,Algorithms,85,45,"Good logic but needs speed improvement"
2024-03-22,HR,Communication,70,30,"Clear communication but somewhat nervous"
2024-03-25,Coding,Data Structures,90,50,"Excellent problem-solving approach"

🔧 Technical Implementation

• Self-Contained Module: Building a new /interview-analytics directory separate from main codebase
• CSV-Based Analysis: Using structured CSV data for all processing
• Streamlit Dashboard: Clean, interactive interface for visualizations
• Python Data Science Stack: Pandas for analysis, Matplotlib/Seaborn for charts

🚀 Key Benefits

• Adds valuable data insights without affecting existing functionality
• Standalone module that can be optionally used
• Clean separation of concerns
• Easy maintenance and future enhancements

This is a self-contained addition that provides advanced analytics without affecting the main application. If you find this helpful and interesting, I request the maintainers to assign this issue to me and provide the GSSOC25 and level label.

Thank you.

[piyush0213]

Thank you for this detailed proposal @vandita-yadav 🙌
This is a fantastic idea that aligns with our vision of making Interview.io not just a mock interview platform but also a data-driven preparation tool.

I’m assigning this issue to you under GSSoC’25 with the label Level: Advanced (30+ pts) since it involves advanced data analysis, visualization, and integration work.

Please make sure to:

• Keep the module self-contained under /interview-analytics
• Document installation & usage steps clearly in README
• Use clean CSV structures so it’s easy for others to extend in future
• Add meaningful visualizations & ensure mobile responsiveness

Looking forward to your contribution 🚀

[vandita-yadav]

@piyush0213

Thank you for the acceptance and the Level 3 assignment!

Quick question about mobile responsiveness: Are you referring to:

• The Streamlit dashboard being mobile-friendly, or
• Planning for future mobile app integration?

Regarding mobile responsiveness clarification:

For the Streamlit dashboard, I'll ensure full mobile-friendly design using:

• use_container_width=True for all visualizations
• Responsive layout testing across different screen sizes
• Clean, adaptive components that work seamlessly on phones/tablets

Regarding mobile app integration:
I should clarify that my current specialization is in data science and Streamlit deployment (including Hugging Face Spaces + Docker) and I am still exploring the app dev. While I'll build a fully mobile-accessible web dashboard, native mobile app integration would be better handled by someone with specific mobile development expertise.

The analytics module will be:

• Self-contained in /interview-analytics
• Mobile-optimized web dashboard accessible from any device
• Accessibility of dashboard through hugging face spaces url
• Well-documented for future mobile integration by other contributors

For the Streamlit dashboard, I'll ensure all visualizations use use_container_width=True and test across screen sizes. Just want to confirm the exact expectation!