DEV Community: Haroon K M

Social Media User Engagement Analysis Dashboard (Power BI)

Haroon K M — Sun, 11 Jan 2026 04:29:40 +0000

This Power BI dashboard analyzes cross-platform social media engagement to help brands understand user behavior, content performance, and audience metrics. The project includes data preprocessing, KPI creation using DAX formulas, and the design of an interactive dashboard.

Project Highlights

Interactive Dashboards: Explore intuitive Power BI dashboards to uncover social media trends.
Key Metrics: Analyze user behaviors, engagement rates, and growth patterns.
Custom Visualizations: Professionally designed visuals tailored for social media insights.
Data-Driven Decision Making: Turn raw social media data into actionable insights.

Technology Stack

Power BI: Primary tool for data modeling, analysis, and visualization.
Data Sources: Social media APIs, CSV, Excel
Integration: Seamless connection between raw data and Power BI dashboards.

Analytics

1. Shares by Platform

Description

This bar chart represents the number of shares on YouTube, TikTok, Instagram, and Twitter.

Analysis

YouTube leads with 1324 shares, making it the most frequently shared platform.
TikTok follows closely with 1260 shares.
Instagram and Twitter trail behind with 1212 and 1204 shares, respectively.
This highlights YouTube's dominance in encouraging users to share content, possibly due to its diverse range of videos that cater to varied audiences.

2. Likes by Platform

Description

The chart showcases the number of likes received on the same four platforms.

Analysis

YouTube again takes the lead with 1324 likes, mirroring its performance in shares.
TikTok remains second with 1260 likes, followed by Instagram (1212 likes) and Twitter (1204 likes).
The consistency between shares and likes suggests a strong correlation between the two metrics, emphasizing YouTube's popularity in driving audience appreciation.

3. Views by Platform

Description

This chart compares the number of views received on YouTube, TikTok, Instagram, and Twitter.

Analysis

YouTube tops the chart with 1324 views, affirming its position as a widely consumed platform.
TikTok comes second (1260 views), followed by Instagram (1212 views) and Twitter (1204 views).
The high number of views on YouTube could be due to its longer format videos, which might engage viewers for extended periods.

4. Comments by Platform

Description

This chart measures the number of comments across the four platforms.

Analysis

YouTube leads once more with 1324 comments, indicating high audience interaction.

TikTok follows at 1260, Instagram at 1212, and Twitter at 1204 comments.

The elevated comment count on YouTube may reflect its focus on discussion and engagement through comment threads.

5. Engagement Level on Content Type over Multiple Platforms

Description

This bar chart examines how various content types (Live Stream, Post, Reel, Shorts, Tweet, Video) perform in terms of engagement across YouTube, TikTok, Instagram, and Twitter.

Analysis

Engagement levels are relatively similar across all platforms and content types, ranging between 0.17K to 0.23K.
There doesn't appear to be a significant variance in interaction between different content types, suggesting a uniform appeal to audiences.
This consistency implies that the platforms have a balanced approach to promoting different content formats.

6. Views by Region

Description

This pie chart reveals the percentage of views from various regions.

Analysis

Germany has the highest percentage of views at 13.54%, closely followed by Japan (13.16%) and Australia (12.94%).
India contributes 12.82% of the views, while Brazil (12.34%), the UK (12.04%), Canada (11.84%), and the USA (11.32%) are slightly lower.
The high numbers for Germany and Japan suggest strong user bases in these regions, perhaps due to regional content appeal or greater platform penetration.

Overall Insight

The data collectively emphasizes YouTube's dominance across user engagement metrics like shares, likes, views, and comments.
It also showcases a relatively uniform engagement across content types and highlights regional differences in user activity.

MomentoMonto

Haroon K M — Sun, 11 Jan 2026 04:25:26 +0000

GitHub

MomentoMonto - Server Monitor & Live Response Time Analyzer

A Realtime Server Monitoring System with Analysis of Response Time Insights which Reduces the Overwhelming CMD Interface for Devs to Monitor their Server in Realtime while Deploying or in their Test Instances

🛡️ Created by Cisco Certified Data Analyst

Skills

Dashboard, Data Analysis, Data Storytelling, Data Visualization, Excel, SQL, Tableau

Overview

MomentoMonto is a server monitoring and response time analysis tool designed to track, analyze, and visualize the performance of web servers in real-time.

It helps developers, system administrators, and network engineers ensure high availability, stability, and speed of their online services.

Key Features

Real-Time Monitoring: Continuously checks server uptime and performance.
Response Time Tracking: Measures latency and displays detailed analysis.
Uptime Visualization: View server stability through charts and metrics.
Multi-Server Support: Monitor multiple endpoints simultaneously.
Smart Alerts (optional): Detect downtime or slow responses automatically.
Modern UI: Interactive Streamlit dashboard for easy data visualization.

Architecture & Workflow

MomentoMonto uses Python and Streamlit to periodically send HTTP requests to the configured servers and record:

Response time (in milliseconds)
Server availability (Up/Down status)
Request timestamps

The collected data is visualized with charts and response trends for intuitive performance tracking.

Tech Stack

Category	Technology
Language	Python
Framework	Streamlit
Libraries	`requests`, `time`, `datetime`, `matplotlib`, `re`
License	Apache 2.0

Contributing

Contributions are always welcome!

If you'd like to enhance MomentoMonto (for example, by adding log exports, push notifications, or cloud sync), feel free to:

Open an issue
Submit a pull request

Author

haroontrailblazer

🔗 GitHub: haroontrailblazer

License

This project is licensed under the Apache License 2.0

StrengthX

Haroon K M — Sun, 11 Jan 2026 04:16:48 +0000

StrengthX

Haroon K M ・ Nov 3 '25

#ai #python #opensource #computerscience

StrengthX

Haroon K M — Mon, 03 Nov 2025 16:47:01 +0000

GitHub

StrengthX - Know How Strong Is Your Passwords

StrengthX is a web application that helps users evaluate the strength of their passwords and check if their passwords have been involved in known data breaches. It provides a quick and secure way to improve password security and promote safe online practices.

🛡️ Created by Google Certified Cybersecurity Engineer

Skills:

Cloud Backup · Cloud Computing · Cloud Security · Compliance Lifecycle · Cybersecurity · Cyber Threat Intelligence · Data Protection · Disaster Recovery · Google Cloud · Identity And Access Management (IAM) · Network Security · Threat Detection

Features

Password Strength Check: Evaluates the complexity of the entered password.
Breach Verification: Checks how many times a password has been exposed in known data breaches.
Privacy-Focused: Passwords are never stored in the backend; all checks are done securely.
Interactive UI: User-friendly interface for seamless password testing.

Contributors

Backend Developers: Haroon K M
Backend Developers: Balamurugan T
Frontend Developer: Sujay S
Documentation: Enbachozhan V
Security Engineer: Harikrishnan V

Technology Stack

Backend: Python
Frontend: Streamlit
AI-Model: ollama
Libraries/Tools: zxcvbn, hashlib, pwnedpasswords, ollama API

Usage

Enter a password in the input field.
View the password strength score and suggestions to improve it.
Check if the password has been exposed in data breaches.
Adjust your password accordingly for better security.
summon SterngthX-Dildo AI to generate a strong password.

Performance Comparison

Legend	🟩	🟨	🟧	⬜
Score	5	4	3	0

Feature	StrengthX	LastPass	1Password	NordPass
AI Password Generation	🟩	⬜	⬜	⬜
Uses zxcvbn	🟩	🟨	🟨	🟨
Uses live breach database	🟩	⬜	⬜	🟧
Regex-based policy enforcement	🟩	🟧	🟧	🟧
Local-only evaluation	🟩	⬜	⬜	⬜

Security & Privacy

OWASP ASVS Password Policy Alignment

Open Worldwide Application Security Project / Application Security Verification Standard.

This password evaluation component has been reviewed against OWASP ASVS v4.0.3 and meets all applicable controls within its defined scope.

ASVS Scope & Level

ASVS Version: 4.0.3
ASVS Level: Level 1 (L1)
Scope: Password evaluation only (no authentication, storage, or sessions)

ASVS Control Mapping

ASVS Control ID	OWASP Requirement	Implementation Detail	Compliance
V2.1.1	Passwords are not stored or processed insecurely	Passwords exist only in volatile memory and are never persisted	✅
V2.1.2	Password strength is evaluated using entropy	Entropy-based evaluation performed using `zxcvbn`	✅
V2.1.3	Breached passwords are detected	Passwords are checked against Have I Been Pwned using k-anonymity	✅
V2.1.4	No insecure composition rules are enforced	No forced uppercase, symbols, or numeric constraints	✅
V2.1.5	Long passphrases are supported	No truncation; long passphrases are fully supported	✅
V2.1.6	Password rotation is not required without compromise	No forced periodic password rotation	✅
V2.1.7	Users are informed about password handling	User-facing disclosure explains secure, non-persistent handling	✅
V6.1.2	Weak cryptographic primitives are not misused	SHA-1 used only for HIBP interoperability, not for storage or auth	✅

Compliance Statement

This password evaluation module is ASVS Level 1–ready under OWASP ASVS v4.0.3.

All applicable password-handling and cryptographic controls are satisfied within the defined scope.

Auditor Notes

Authentication, session management, and authorization are intentionally out of scope
SHA-1 usage is strictly limited to external breach detection compatibility
No password data is logged, rendered, or persisted
All passwords are processed locally in the browser and hashed before any breach verification.
No user passwords or sensitive information are stored on the server.
Fully Fully compliant with OWASP Password Guidelines

License

This project is licensed under the Apache License 2.0.

Contact

For queries or contributions, feel free to contact the contributors listed above or open an issue in the repository.

StrengthX – Empowering safer online practices, one password at a time.