DEV Community: Jahid Hasan

🌟 ɪɴᴛʀᴏᴅᴜᴄɪɴɢ ᴛʜᴇ ʀᴏꜱᴇ ᴘɪɴᴇ ᴛʜᴇᴍᴇ ꜰᴏʀ ʀ: ᴇɴʜᴀɴᴄɪɴɢ ɢɢᴘʟᴏᴛ2 ᴠɪꜱᴜᴀʟɪᴢᴀᴛɪᴏɴꜱ ᴡɪᴛʜ ᴇʟᴇɢᴀɴᴛ ᴍɪɴɪᴍᴀʟɪꜱᴍ 🌟

Jahid Hasan — Thu, 28 Nov 2024 03:37:04 +0000

🚀 I’m thrilled to present the Rose Pine Theme for R, a custom aesthetic for ggplot2 that blends modern minimalism with the serene elegance of the Rose Pine design philosophy. Drawing inspiration from its Python counterpart, this R-specific implementation has been refined to align with ggplot2’s unique functionality.

Python Link: https://lnkd.in/ehjKM3mB

🎨 Key Features
Sophisticated Aesthetic: Offers a calm, polished appearance, perfect for professional and academic visualizations.
Rich Color Palette: Integrates the signature Rose Pine color scheme, complemented by customizable accent tones to suit diverse datasets.
Seamless Integration: Designed to effortlessly fit into standard ggplot2 workflows, saving you time while enhancing your plots.

📚 How to Use
Installing and applying the Rose Pine Theme is straightforward, and the result is a visually striking output tailored for high-impact presentations or publications. Here’s a glimpse of the theme in action, showcasing its versatility across different types of data.

🔗 Documentation and Examples

Comprehensive documentation, including implementation guidelines, is available in the GitHub Repository. Check it out to explore usage tips and visual examples.

Documentation: https://lnkd.in/ekTNmS7d

💡 Collaborate with Me

Feedback and contributions are highly encouraged! Whether you have suggestions for enhancements or want to contribute directly to the project, feel free to submit a pull request or open an issue on GitHub. Collaboration is the key to growth!

📩 Get in Touch
For any questions, feedback, or collaborative ideas, reach out via my portfolio website or connect with me on GitHub.

portfolio: https://msjahid.me/

🔗 GitHub Repository: Explore the Rose Pine Theme for R
https://lnkd.in/eEYU6QR8

hashtag#DataVisualization hashtag#ggplot2 hashtag#RStats hashtag#RosePine hashtag#DataScience hashtag#OpenSource hashtag#RCommunity hashtag#VisualizationDesign hashtag#RPackages hashtag#DataViz

LANGUAGE DETECTION SYSTEM

Jahid Hasan — Mon, 25 Nov 2024 07:30:15 +0000

I’m thrilled to share what I’ve been working on! 🚀

I’ve been diving into a Kaggle dataset for language detection, which includes 17 different languages. After experimenting with several machine learning algorithms, Multinomial Naive Bayes came out on top, delivering an impressive 98% accuracy in identifying the correct language. 📊✨

🔗 Explore LANGUAGE DETECTION SYSTEM:
Live Demo: https://lnkd.in/eTQvigBA

𝘾𝙖𝙩𝙘𝙝 𝙩𝙝𝙚 𝙫𝙞𝙙𝙚𝙤 𝙛𝙤𝙧 𝙖 𝙦𝙪𝙞𝙘𝙠 𝙩𝙤𝙪𝙧! 🎥👇

Next up, I’m planning to build a web application using Flask to make the model more accessible. The app will include a neat feature: if the input language isn’t one of the 17 in the dataset, it will display a custom message like “Unable to detect language.” Plus, I’m adding a confidence level to show how certain the model is about its predictions. 💡

For the front-end, I’ll use HTML, CSS, and a bit of JavaScript to keep it simple, user-friendly, and responsive. I can’t wait to bring this idea to life and make language detection easier and more accessible for everyone! 🌍✨

📌 ᴀʀᴇ ʏᴏᴜ ɪɴᴛᴏ ᴅᴀᴛᴀ ꜱᴄɪᴇɴᴄᴇ, ᴀɴᴀʟʏᴛɪᴄꜱ, ᴏʀ ᴠɪꜱᴜᴀʟɪᴢᴀᴛɪᴏɴ? ʟᴇᴛ’ꜱ ᴄᴏɴɴᴇᴄᴛ—ɪ’ᴅ ʟᴏᴠᴇ ᴛᴏ ᴄʜᴀᴛ ᴍᴏʀᴇ ᴀʙᴏᴜᴛ ɪᴛ!

hashtag#MachineLearning hashtag#Flask hashtag#Kaggle hashtag#LanguageDetection hashtag#DataScience hashtag#WebDev hashtag#NaiveBayes

💻📊 Effortless Data Integration with Pandas and PostgreSQL 🚀

Jahid Hasan — Thu, 21 Nov 2024 16:01:59 +0000

Hey friends! Just wrapped up a quick demo showcasing how to combine the magic of Pandas and PostgreSQL for smooth and efficient data workflows. Here's the gist:

Imagine loading data from a CSV file, connecting to a PostgreSQL database, and inserting it into a table—all in a few simple steps. With tools like bpython for interactive coding and pgcli for querying the database, Python makes this whole process a breeze.

🎥 Check out the full Asciinema recording here: https://lnkd.in/ewMimwXh

🎥 What I Did:

1️⃣ Set Up PostgreSQL and Tables
Used pgcli (it’s like a turbo-charged psql) to connect to my PostgreSQL database and inspect the existing tables.
Took a peek at the structure of the students table before updating it with new data.

2️⃣ Read CSV Data with Pandas
Loaded student data from a CSV file using Pandas.
It's so simple—just pd.read_csv() and you're good to go.

3️⃣ Dynamic Database Connection
Pulled the PostgreSQL connection details securely from a connection.txt file (because, you know, hashtag#StaySecure).
Then connected to the database with SQLAlchemy—a solid library for Python-PostgreSQL integration.

4️⃣ Query and Verify
Queried the students table to see what’s already there.
Loaded the data into a Pandas DataFrame for inspection. Easy visualization and checks!

5️⃣ Insert Data with Pandas
Inserted the new data from the CSV into PostgreSQL using Pandas’ .to_sql() function.
Used the if_exists='replace' option to overwrite existing data, ensuring a fresh start.

6️⃣ Validation
Back to pgcli to verify everything. The new data was successfully added to the table. 🎉

🔥 Workflows like this are pure gold for data engineers and scientists working with relational databases. It’s fast, clean, and flexible.
Your Turn:

What tools and tricks do you use for database workflows? Do you have any tips for making things even more efficient? Let me know in the comments below! 👇

hashtag#Python hashtag#PostgreSQL hashtag#DataEngineering hashtag#SQLAlchemy hashtag#Pandas hashtag#Linux

🚀 LoanEase: Simplifying Loan Approvals with Machine Learning

Jahid Hasan — Wed, 20 Nov 2024 12:35:36 +0000

A sleek web app built with Flask, leveraging Gaussian Naive Bayes for instant, reliable loan approval predictions. It’s deployed on Railway, ensuring seamless access for all.

🌟 Key Features:

User-Friendly Design: Simple loan application form with real-time validation.
AI-Powered Insights: Predictions made using a trained machine learning model.
Personalized Feedback: Tailored results based on your input.
Error Handling: Intuitive error pages guide users effectively.
Cloud Accessibility: Deployed online, fully containerized with Docker.

🔧 Tech Stack:

Backend: Python + Flask
Frontend: HTML/CSS for clean and responsive UI
ML Model: Gaussian Naive Bayes for classification
Deployment: Docker for containerization and Railway for hosting

📋 How It Works:

Fill out a simple form.
Inputs are validated to ensure accuracy.
The machine learning model predicts your loan status.
Receive instant, personalized feedback.

🎉 Highlights of Your Journey:

Built an end-to-end machine learning pipeline.
Gained hands-on experience in Docker and cloud deployment.

🔗 Explore LoanEase:
Live Demo: https://lnkd.in/ed4P8gFG

𝘾𝙖𝙩𝙘𝙝 𝙩𝙝𝙚 𝙫𝙞𝙙𝙚𝙤 𝙛𝙤𝙧 𝙖 𝙦𝙪𝙞𝙘𝙠 𝙩𝙤𝙪𝙧! 🎥👇

hashtag#MachineLearning hashtag#Flask hashtag#Python hashtag#Docker hashtag#CloudDeployment hashtag#LoanEase hashtag#AI hashtag#CareerGrowth