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This is Raabia

Computer Science undergrad at FAST-NUCES. I love to code as an hobby and profession. On my path to explore this field, I am working on AI projects. Studying Information Retrieval and Text Mining have ignited new ideas to me. 🤓

Github

  • 🔭 I’m currently working on Product Title Classification - Lazada Dataset
  • 📃 I am improving my coding skills in Python Language.
  • 🌱 I've learnt React & Database Management On SQL
  • 🕸️Used Databases MySQL
  • 💬 Ask me about C, C++, Python, HTML, CSS, JS
  • 📚 Solve LeetCode in leasure time.
  • 📫 How to reach me [email protected]

Repositories:

Websites 📄:

Languages and Tools:

angular angularjs bootstrap cplusplus csharp css3 git html5 javascript linux mssql mysql nestjs nodejs postman python

sraabia

sraabia

Syeda Rabia's Projects

c-algorithms icon c-algorithms

An experimental C library of common data structures and algorithms

codeforces icon codeforces

this repo will contain some of my codeforces solutions, each file name is the name of the problem on codeforces.

codesignals icon codesignals

Here i upload solutions of practice interview questions.

database-labs icon database-labs

I will share my database query assignments of theory and labs. I have written all the queries by myself so there might be some errors. I have tested the queries on Oracle.

fullstack-course4 icon fullstack-course4

Example code for HTML, CSS, and Javascript for Web Developers Coursera Course

os-labs icon os-labs

Operating Systems Labs || NUCES- FAST || THIRD SEMESTER LABS

os-project icon os-project

Parallel Programming Comparison of Algorithms using Pthreads vs. OpenMP vs. serial

projects icon projects

Coding Projects in Programming Languages

sraabia.github.io icon sraabia.github.io

This repository is created for submitting the solutions of the assignments of the course in Coursera.

vector-space-model-vsm- icon vector-space-model-vsm-

Vector Space Model(VSM) is a mathematical model often used in information retrieval and natural language processing, which ranks documents by computing TF-IDF scores and Cosine Similarity.

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