๐“ฆ๐“ฎ๐“ต๐“ฌ๐“ธ๐“ถ๐“ฎ ๐“ฝ๐“ธ ๐“ถ๐”‚ ๐“Ÿ๐“ธ๐“ป๐“ฝ๐“ฏ๐“ธ๐“ต๐“ฒ๐“ธ

๐–„๐–”๐–š'๐–›๐–Š ๐•ต๐–š๐–˜๐–™ ๐•ฐ๐–“๐–™๐–Š๐–—๐–Š๐–‰: ๐•ฟ๐–๐–Š ๐•ฎ๐–”๐–‰๐–Š๐–›๐–Š๐–—๐–˜๐–Š ๐–”๐–‹ ๐•พ๐–—๐–Ž๐–๐–†๐–“ ๐•ฝ๐–”๐–ž

๐““๐“ฎ๐“ผ๐“ฒ๐“ฐ๐“ท๐“ฎ๐“ญ, ๐““๐“ฎ๐“ฟ๐“ฎ๐“ต๐“ธ๐“น๐“ฎ๐“ญ & ๐““๐“ฎ๐“น๐“ต๐“ธ๐”‚๐“ฎ๐“ญ ๐”€๐“ฒ๐“ฝ๐“ฑ ๐“Ÿ๐“ป๐“ฎ๐“ฌ๐“ฒ๐“ผ๐“ฒ๐“ธ๐“ท

๐€๐ฅ๐ฅ ๐‘๐ข๐ ๐ก๐ญ๐ฌ ๐‘๐ž๐ฌ๐ž๐ซ๐ฏ๐ž๐

Hi๐Ÿ‘‹,
My name is ๐’๐ซ๐ข๐ฃ๐š๐ง ๐‘๐จ๐ฒ,
I am

Srijan Image

๐˜ผ๐™—๐™ค๐™ช๐™ฉ โžก

๐Ÿ“ ๐™Ž๐™๐™ˆ๐™ˆ๐˜ผ๐™๐™” ๐Ÿ“

I am a ๐˜ฑ๐˜ข๐˜ด๐˜ด๐˜ช๐˜ฐ๐˜ฏ๐˜ข๐˜ต๐˜ฆ ๐˜Š๐˜ฐ๐˜ฎ๐˜ฑ๐˜ถ๐˜ต๐˜ฆ๐˜ณ ๐˜š๐˜ค๐˜ช๐˜ฆ๐˜ฏ๐˜ค๐˜ฆ & ๐˜Œ๐˜ฏ๐˜จ๐˜ช๐˜ฏ๐˜ฆ๐˜ฆ๐˜ณ๐˜ช๐˜ฏ๐˜จ ๐˜ด๐˜ต๐˜ถ๐˜ฅ๐˜ฆ๐˜ฏ๐˜ต ๐˜ข๐˜ต ๐˜๐˜Œ๐˜”, ๐˜’๐˜ฐ๐˜ญ๐˜ฌ๐˜ข๐˜ต๐˜ข, with a keen interest in Artificial Intelligence and Machine Learning development. Eager to explore the realms of ๐€๐ˆ ๐š๐ง๐ ๐Œ๐‹, I am committed to leveraging technology to drive innovation and solve complex problems.

๐Ÿ’ก ๐˜ผ๐™ง๐™š๐™–๐™จ ๐™ค๐™› ๐™„๐™ฃ๐™ฉ๐™š๐™ง๐™š๐™จ๐™ฉ ๐Ÿ’ก:
โœ…๐˜ˆ๐˜ณ๐˜ต๐˜ช๐˜ง๐˜ช๐˜ค๐˜ช๐˜ข๐˜ญ ๐˜๐˜ฏ๐˜ต๐˜ฆ๐˜ญ๐˜ญ๐˜ช๐˜จ๐˜ฆ๐˜ฏ๐˜ค๐˜ฆ.
โœ…๐˜”๐˜ข๐˜ค๐˜ฉ๐˜ช๐˜ฏ๐˜ฆ ๐˜“๐˜ฆ๐˜ข๐˜ณ๐˜ฏ๐˜ช๐˜ฏ๐˜จ.
โœ…๐˜‹๐˜ข๐˜ต๐˜ข ๐˜š๐˜ค๐˜ช๐˜ฆ๐˜ฏ๐˜ค๐˜ฆ.
โœ…๐˜•๐˜ข๐˜ต๐˜ถ๐˜ณ๐˜ข๐˜ญ ๐˜“๐˜ข๐˜ฏ๐˜จ๐˜ถ๐˜ข๐˜จ๐˜ฆ ๐˜—๐˜ณ๐˜ฐ๐˜ค๐˜ฆ๐˜ด๐˜ด๐˜ช๐˜ฏ๐˜จ.
โœ…๐˜ˆ๐˜จ๐˜ฆ๐˜ฏ๐˜ต๐˜ช๐˜ค ๐˜ˆ๐˜ / ๐˜Ž๐˜ฆ๐˜ฏ ๐˜ˆ๐˜.

๐Ÿ“Œ ๐™‹๐™Š๐™Ž๐™„๐™๐™„๐™Š๐™‰๐™Ž ๐Ÿ“Œ:
โœ…๐˜”๐˜ฆ๐˜ฎ๐˜ฃ๐˜ฆ๐˜ณ ๐˜ฐ๐˜ง ๐˜—๐˜ญ๐˜ข๐˜ค๐˜ฆ๐˜ฎ๐˜ฆ๐˜ฏ๐˜ต ๐˜‹๐˜ฆ๐˜ฑ๐˜ข๐˜ณ๐˜ต๐˜ฎ๐˜ฆ๐˜ฏ๐˜ต ๐˜Š๐˜ฐ๐˜ฎ๐˜ฎ๐˜ถ๐˜ฏ๐˜ช๐˜ต๐˜บ ๐˜๐˜Œ๐˜”-๐˜œ๐˜Œ๐˜” ๐˜Ž๐˜ณ๐˜ฐ๐˜ถ๐˜ฑ.
โœ…๐˜๐˜ฏ๐˜ฅ๐˜ถ๐˜ด๐˜ต๐˜ณ๐˜บ ๐˜Š๐˜ฐ๐˜ฎ๐˜ฎ๐˜ถ๐˜ฏ๐˜ช๐˜ต๐˜บ ๐˜ฎ๐˜ฆ๐˜ฎ๐˜ฃ๐˜ฆ๐˜ณ ๐˜ฐ๐˜ง ๐˜๐˜Œ๐˜” ๐˜Ž๐˜ฆ๐˜ฏ๐˜ˆ๐˜ ๐˜Š๐˜–๐˜Œ.
โœ…๐˜‹๐˜ข๐˜ต๐˜ข ๐˜ˆ๐˜ฏ๐˜ข๐˜ญ๐˜บ๐˜ด๐˜ต ๐˜๐˜ฏ๐˜ต๐˜ฆ๐˜ณ๐˜ฏ ๐˜ข๐˜ต ๐˜›๐˜ฆ๐˜ค๐˜ฉ๐˜ด๐˜ฅ ๐˜๐˜ฏ๐˜ฏ๐˜ฐ๐˜ท๐˜ข๐˜ต๐˜ช๐˜ฐ๐˜ฏ๐˜ด ๐˜Š๐˜ฐ๐˜ฏ๐˜ด๐˜ถ๐˜ญ๐˜ต๐˜ช๐˜ฏ๐˜จ ๐˜—๐˜ท๐˜ต ๐˜“๐˜ช๐˜ฎ๐˜ช๐˜ต๐˜ฆ๐˜ฅ ๐˜ธ๐˜ฐ๐˜ณ๐˜ฌ๐˜ช๐˜ฏ๐˜จ ๐˜ถ๐˜ฏ๐˜ฅ๐˜ฆ๐˜ณ ๐˜ข ๐˜ฑ๐˜ณ๐˜ฐ๐˜ซ๐˜ฆ๐˜ค๐˜ต ๐˜ช๐˜ฏ ๐˜๐˜ช๐˜ฏ๐˜ฅ๐˜ถ๐˜ด๐˜ต๐˜ข๐˜ฏ ๐˜Š๐˜ฐ๐˜ฑ๐˜ฑ๐˜ฆ๐˜ณ ๐˜“๐˜ช๐˜ฎ๐˜ช๐˜ต๐˜ฆ๐˜ฅ - ๐˜Ž๐˜ฐ๐˜ท๐˜ต ๐˜ฐ๐˜ง ๐˜๐˜ฏ๐˜ฅ๐˜ช๐˜ข .
โœ…๐˜”๐˜ฆ๐˜ฎ๐˜ฃ๐˜ฆ๐˜ณ ๐˜ฐ๐˜ง ๐˜Ž๐˜ฐ๐˜ฐ๐˜จ๐˜ญ๐˜ฆ ๐˜‹๐˜ฆ๐˜ท๐˜ฆ๐˜ญ๐˜ฐ๐˜ฑ๐˜ฆ๐˜ณ ๐˜Ž๐˜ณ๐˜ฐ๐˜ถ๐˜ฑ,๐˜๐˜Œ๐˜”.
โœ…๐˜ˆ๐˜ณ๐˜ต๐˜ช๐˜ง๐˜ช๐˜ค๐˜ช๐˜ข๐˜ญ ๐˜ช๐˜ฏ๐˜ต๐˜ฆ๐˜ญ๐˜ญ๐˜ช๐˜จ๐˜ฆ๐˜ฏ๐˜ค๐˜ฆ ๐˜๐˜ฏ๐˜ต๐˜ฆ๐˜ณ๐˜ฏ ๐˜ข๐˜ต ๐˜—๐˜ช๐˜ฏ๐˜ฏ๐˜ข๐˜ค๐˜ญ๐˜ฆ ๐˜“๐˜ข๐˜ฃ๐˜ด.
โœ…๐˜”๐˜ข๐˜ค๐˜ฉ๐˜ช๐˜ฏ๐˜ฆ ๐˜“๐˜ฆ๐˜ข๐˜ณ๐˜ฏ๐˜ช๐˜ฏ๐˜จ ๐˜๐˜ฏ๐˜ต๐˜ฆ๐˜ณ๐˜ฏ ๐˜ข๐˜ต ๐˜š๐˜ฌ๐˜ช๐˜ญ๐˜ญ๐˜ค๐˜ณ๐˜ข๐˜ง๐˜ต ๐˜›๐˜ฆ๐˜ค๐˜ฉ๐˜ฏ๐˜ฐ๐˜ญ๐˜ฐ๐˜จ๐˜บ.
โœ…๐˜”๐˜ฆ๐˜ฎ๐˜ฃ๐˜ฆ๐˜ณ ๐˜ฐ๐˜ง ๐˜Š๐˜ฐ๐˜ฅ๐˜ช๐˜ฏ๐˜จ ๐˜Š๐˜ญ๐˜ถ๐˜ฃ ๐˜๐˜ฏ๐˜ฅ๐˜ช๐˜ข.
โœ…๐˜Š๐˜ข๐˜ฎ๐˜ฑ๐˜ถ๐˜ด ๐˜ˆ๐˜ฎ๐˜ฃ๐˜ข๐˜ด๐˜ด๐˜ข๐˜ฅ๐˜ฐ๐˜ณ ๐˜ง๐˜ฐ๐˜ณ ๐˜Ž๐˜ช๐˜ณ๐˜ญ๐˜š๐˜ค๐˜ณ๐˜ช๐˜ฑ๐˜ต ๐˜š๐˜ถ๐˜ฎ๐˜ฎ๐˜ฆ๐˜ณ ๐˜ฐ๐˜ง ๐˜Š๐˜ฐ๐˜ฅ๐˜ฆ 2025.
โœ…๐˜”๐˜ข๐˜ค๐˜ฉ๐˜ช๐˜ฏ๐˜ฆ ๐˜“๐˜ฆ๐˜ข๐˜ณ๐˜ฏ๐˜ช๐˜ฏ๐˜จ ๐˜๐˜ฏ๐˜ต๐˜ฆ๐˜ณ๐˜ฏ ๐˜ข๐˜ต ๐˜๐˜ฏ๐˜ต๐˜ฆ๐˜ณ๐˜ฏ ๐˜—๐˜ฆ.
โœ…๐˜š๐˜ถ๐˜ฎ๐˜ฎ๐˜ฆ๐˜ณ ๐˜๐˜ฏ๐˜ต๐˜ฆ๐˜ณ๐˜ฏ ๐˜ข๐˜ต ๐˜๐˜Œ๐˜” - ๐˜๐˜Œ๐˜‹๐˜Š,๐˜Ž๐˜ฆ๐˜ฏ๐˜ˆ๐˜ ๐˜Š๐˜–๐˜Œ.
โœ…๐˜”๐˜ข๐˜ค๐˜ฉ๐˜ช๐˜ฏ๐˜ฆ ๐˜“๐˜ฆ๐˜ข๐˜ณ๐˜ฏ๐˜ช๐˜ฏ๐˜จ ๐˜๐˜ฏ๐˜ต๐˜ฆ๐˜ณ๐˜ฏ ๐˜ข๐˜ต ๐˜œ๐˜ฏ๐˜ช๐˜ง๐˜ช๐˜ฆ๐˜ฅ ๐˜”๐˜ฆ๐˜ฏ๐˜ต๐˜ฐ๐˜ณ.
โœ…๐˜Ž๐˜ฆ๐˜ฏ๐˜ฆ๐˜ณ๐˜ข๐˜ต๐˜ช๐˜ท๐˜ฆ ๐˜ˆ๐˜ ๐˜๐˜ฏ๐˜ต๐˜ฆ๐˜ณ๐˜ฏ ๐˜ข๐˜ต ๐˜—๐˜ณ๐˜ฐ๐˜ฅ๐˜ช๐˜จ๐˜บ ๐˜๐˜ฏ๐˜ง๐˜ฐ๐˜ต๐˜ฆ๐˜ค๐˜ฉ.
โœ…๐˜”๐˜ข๐˜ค๐˜ฉ๐˜ช๐˜ฏ๐˜ฆ ๐˜“๐˜ฆ๐˜ข๐˜ณ๐˜ฏ๐˜ช๐˜ฏ๐˜จ ๐˜๐˜ฏ๐˜ต๐˜ฆ๐˜ณ๐˜ฏ ๐˜ข๐˜ต ๐˜Š๐˜ฐ๐˜ฅ๐˜ฆ ๐˜ข๐˜ญ๐˜ฑ๐˜ฉ๐˜ข.
โœ…๐˜—๐˜บ๐˜ต๐˜ฉ๐˜ฐ๐˜ฏ ๐˜‹๐˜ฆ๐˜ท๐˜ฆ๐˜ญ๐˜ฐ๐˜ฑ๐˜ฆ๐˜ณ ๐˜๐˜ฏ๐˜ต๐˜ฆ๐˜ณ๐˜ฏ ๐˜ข๐˜ต ๐˜๐˜Œ๐˜” ๐˜Ž๐˜ฆ๐˜ฏ๐˜ˆ๐˜ ๐˜Š๐˜–๐˜Œ.
โœ…๐˜”๐˜ข๐˜ค๐˜ฉ๐˜ช๐˜ฏ๐˜ฆ ๐˜“๐˜ฆ๐˜ข๐˜ณ๐˜ฏ๐˜ช๐˜ฏ๐˜จ ๐˜๐˜ฏ๐˜ต๐˜ฆ๐˜ณ๐˜ฏ ๐˜ข๐˜ต ๐˜ˆ๐˜“๐˜๐˜๐˜‹๐˜– ๐˜›๐˜ฆ๐˜ค๐˜ฉ.
โœ…๐˜‘๐˜ถ๐˜ฏ๐˜ช๐˜ฐ๐˜ณ ๐‘…๐˜ฆ๐˜ด๐˜ฆ๐˜ข๐˜ณ๐˜ค๐˜ฉ ๐˜ˆ๐˜ด๐˜ด๐˜ช๐˜ด๐˜ต๐˜ข๐˜ฏ๐˜ต ๐˜ข๐˜ต ๐˜๐˜Œ๐˜‹๐˜Š ๐˜“๐˜ข๐˜ฃ, ๐˜๐˜Œ๐˜”.

๐Ÿ† ๐˜ผ๐˜พ๐™ƒ๐™„๐™€๐™‘๐™€๐™ˆ๐™€๐™‰๐™๐™Ž ๐Ÿ†

  • โœ…Attended Study Abroad Program at ๐™‰๐™–๐™ฉ๐™ž๐™ค๐™ฃ๐™–๐™ก ๐™๐™ฃ๐™ž๐™ซ๐™š๐™ง๐™จ๐™ž๐™ฉ๐™ฎ ๐™Š๐™› ๐™Ž๐™ž๐™ฃ๐™œ๐™–๐™ฅ๐™ค๐™ง๐™š (๐™‰๐™๐™Ž)

  • โœ…Achieved Certifications based on ๐˜ผ๐™„/๐™ˆ๐™‡,๐˜พ๐™ฎ๐™—๐™š๐™ง๐™Ž๐™š๐™˜๐™ช๐™ง๐™ž๐™ฉ๐™ฎ,๐˜ฟ๐™–๐™ฉ๐™–๐™Ž๐™˜๐™ž๐™š๐™ฃ๐™˜๐™š ๐™๐™ง๐™ค๐™ข ๐˜พ๐™ค๐™ช๐™ง๐™จ๐™š๐™ง๐™–,๐™‡๐™ž๐™ฃ๐™ ๐™š๐™™๐™ก๐™ฃ ๐™‡๐™š๐™–๐™ง๐™ฃ๐™ž๐™ฃ๐™œ,๐˜ฟ๐™š๐™ก๐™ค๐™ž๐™ฉ๐™ฉ๐™š

  • โœ…Solved DSA problems on platforms like๐™‡๐™š๐™š๐™ฉ๐˜พ๐™ค๐™™๐™š, ๐™ƒ๐™–๐™˜๐™ ๐™š๐™ง๐™๐™–๐™ฃ๐™ .

  • โœ…Completed Internships on ๐˜ผ๐™ง๐™ฉ๐™ž๐™›๐™ž๐™˜๐™ž๐™–๐™ก ๐™ž๐™ฃ๐™ฉ๐™š๐™ก๐™ก๐™ž๐™œ๐™š๐™ฃ๐™˜๐™š,๐™ˆ๐™–๐™˜๐™๐™ž๐™ฃ๐™š ๐™‡๐™š๐™–๐™ง๐™ฃ๐™ž๐™ฃ๐™œ ๐™–๐™ฃ๐™™ ๐™‹๐™ฎ๐™ฉ๐™๐™ค๐™ฃ ๐˜ฟ๐™š๐™ซ๐™š๐™ก๐™ค๐™ฅ๐™š๐™ง

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„๐•–๐•ค๐•ฆ๐•ž๐•–

๐Ÿ› ๏ธ๐Ÿง  ๐™Ž๐™†๐™„๐™‡๐™‡๐™Ž ๐Ÿ› ๏ธ๐Ÿง 

๐Ÿง‘โ€๐Ÿซ ๐˜พ๐™Š๐™๐™๐™Ž๐™€๐™’๐™Š๐™๐™† ๐Ÿง‘โ€๐Ÿซ

  • ๐ŸŸข๐˜‹๐˜ข๐˜ต๐˜ข ๐˜š๐˜ต๐˜ณ๐˜ถ๐˜ค๐˜ต๐˜ถ๐˜ณ๐˜ฆ๐˜ด & ๐˜ˆ๐˜ญ๐˜จ๐˜ฐ๐˜ณ๐˜ช๐˜ต๐˜ฉ๐˜ฎ๐˜ด 90%
  • ๐ŸŸข๐˜–๐˜ฃ๐˜ซ๐˜ฆ๐˜ค๐˜ต-๐˜–๐˜ณ๐˜ช๐˜ฆ๐˜ฏ๐˜ต๐˜ฆ๐˜ฅ ๐˜—๐˜ณ๐˜ฐ๐˜จ๐˜ณ๐˜ข๐˜ฎ๐˜ฎ๐˜ช๐˜ฏ๐˜จ 85%
  • ๐ŸŸข๐˜”๐˜ข๐˜ค๐˜ฉ๐˜ช๐˜ฏ๐˜ฆ ๐˜“๐˜ฆ๐˜ข๐˜ณ๐˜ฏ๐˜ช๐˜ฏ๐˜จ 90%
  • ๐ŸŸข๐˜ˆ๐˜ณ๐˜ต๐˜ช๐˜ง๐˜ช๐˜ค๐˜ช๐˜ข๐˜ญ ๐˜๐˜ฏ๐˜ต๐˜ฆ๐˜ญ๐˜ญ๐˜ช๐˜จ๐˜ฆ๐˜ฏ๐˜ค๐˜ฆ 80%
  • ๐ŸŸข๐˜•๐˜ข๐˜ต๐˜ถ๐˜ณ๐˜ข๐˜ญ ๐˜“๐˜ข๐˜ฏ๐˜จ๐˜ถ๐˜ข๐˜จ๐˜ฆ ๐˜—๐˜ณ๐˜ฐ๐˜ค๐˜ฆ๐˜ด๐˜ด๐˜ช๐˜ฏ๐˜จ 75%
  • ๐ŸŸข๐˜‹๐˜ข๐˜ต๐˜ข๐˜ฃ๐˜ข๐˜ด๐˜ฆ ๐˜”๐˜ข๐˜ฏ๐˜ข๐˜จ๐˜ฆ๐˜ฎ๐˜ฆ๐˜ฏ๐˜ต ๐˜š๐˜บ๐˜ด๐˜ต๐˜ฆ๐˜ฎ 88%

๐ŸŸก๐™‹๐™๐™Š๐™‚๐™๐˜ผ๐™ˆ๐™ˆ๐™„๐™‰๐™‚ ๐™Ž๐™†๐™„๐™‡๐™‡๐™Ž๐ŸŸก

  • ๐ŸŸข๐˜ฑ๐˜บ๐˜ต๐˜ฉ๐˜ฐ๐˜ฏ 95%
  • ๐ŸŸข๐˜‘๐˜ข๐˜ท๐˜ข 85%
  • ๐ŸŸข๐˜Š-๐˜“๐˜ข๐˜ฏ๐˜จ๐˜ถ๐˜ข๐˜จ๐˜ฆ 90%
  • ๐ŸŸข๐˜”๐˜“ 90%

๐Ÿค๐™„๐™‰๐™๐™€๐™๐™‰๐™Ž๐™ƒ๐™„๐™‹๐™Ž๐Ÿค

๐ŸŽ“ ๐™‹๐™๐™Š๐™…๐™€๐˜พ๐™๐™Ž ๐ŸŽ“

๐Ÿ˜ก๐Ÿ˜๐Ÿ˜”๐Ÿ™‚โ€โ†• ๐“”๐“ถ๐“ธ๐“ฝ๐“ฒ๐“ธ๐“ท ๐“ก๐“ฎ๐“ฌ๐“ธ๐“ฐ๐“ท๐“ฒ๐“ฝ๐“ฒ๐“ธ๐“ท ๐“•๐“ป๐“ธ๐“ถ ๐“ข๐“น๐“ฎ๐“ฎ๐“ฌ๐“ฑ

Description: A machine learning project for recognizing emotions from speech by analyzing vocal features like pitch, tone, and intensity to enhance human-computer interaction and emotional analysis.

Tools & Technologies:
Python Libraries:Libraries like librosa, numpy, scikit-learn, matplotlib for audio processing, numerical tasks, ML utilities, and plotting. Deep Learning Framework :TensorFlow and its high-level API Keras for building and training the LSTM deep learning model.
KaggleHub : kagglehub is utilized to directly download the RAVDESS emotional speech audio dataset,

๐Ÿ‘€ View Project

โš•๐Ÿฉบ๐Ÿ’Š ๐““๐“ฒ๐“ผ๐“ฎ๐“ช๐“ผ๐“ฎ ๐“Ÿ๐“ป๐“ฎ๐“ญ๐“ฒ๐“ฌ๐“ฝ๐“ฒ๐“ธ๐“ท ๐“•๐“ป๐“ธ๐“ถ ๐“œ๐“ฎ๐“ญ๐“ฒ๐“ฌ๐“ช๐“ต ๐““๐“ช๐“ฝ๐“ช

Description: A machine learning model that predict diseases using patient data for early detection, better outcomes, and optimized healthcare resources.

Tools & Technologies:
Python Libraries : libraries like pandas, scikit-learn, matplotlib, and seaborn for data handling, preprocessing, and visualization.
TensorFlow/Keras : For building and training the neural network model. StandardScaler and LabelEncoder: For feature scaling and target encoding.

๐Ÿ‘€ View Project

โš•๐Ÿฉบ๐“‘๐“ป๐“ฎ๐“ช๐“ผ๐“ฝ ๐“’๐“ช๐“ท๐“ฌ๐“ฎ๐“ป ๐““๐“ฎ๐“ฝ๐“ฎ๐“ฌ๐“ฝ๐“ฒ๐“ธ๐“ท ๐“๐“น๐“น

Description: A machine learning model that predicts breast cancer using medical data for early detection and improved treatment outcomes.

Tools & Technologies:
Python Libraries : libraries like pandas, scikit-learn, matplotlib, and seaborn for data handling, preprocessing, and visualization.
TensorFlow/Keras : For building and training the neural network model. StandardScaler and LabelEncoder: For feature scaling and target encoding.

๐Ÿ‘€ View Project

๐Ÿฅ ๐““๐“ฒ๐“ช๐“ซ๐“ฎ๐“ฝ๐“ฎ๐“ผ ๐“Ÿ๐“ป๐“ฎ๐“ญ๐“ฒ๐“ฌ๐“ฝ๐“ฒ๐“ธ๐“ท ๐“ค๐“ผ๐“ฒ๐“ท๐“ฐ ๐“œ๐“› ๐’œ๐’ซ๐ผ

Description: A machine learning model that predicts diabetes using patient data for early detection, improved treatment planning, and better healthcare outcomes.

Tools & Technologies:
Python Libraries: Used libraries like pandas, scikit-learn, matplotlib, and seaborn for data loading, preprocessing, and visualization.
Scikit-learn Models: Applied Logistic Regression, Random Forest, and other classifiers for prediction.
StandardScaler and LabelEncoder: For feature scaling and encoding categorical data.
Jupyter Notebook/Google Colab: Used as the development environment for writing and testing code.

๐Ÿ‘€ View Project

๐ŸŒค๏ธ ๐“ฆ๐“ฎ๐“ช๐“ฝ๐“ฑ๐“ฎ๐“ป ๐“Ÿ๐“พ๐“ต๐“ผ๐“ฎ ๐“Ÿ๐“ป๐“ธ - ๐“ก๐“ฎ๐“ช๐“ต ๐“ฃ๐“ฒ๐“ถ๐“ฎ ๐“ฆ๐“ฎ๐“ช๐“ฝ๐“ฑ๐“ฎ๐“ป ๐“•๐“ธ๐“ป๐“ฎ๐“ฌ๐“ช๐“ผ๐“ฝ๐“ฒ๐“ท๐“ฐ ๐“๐“น๐“น

Description: A sleek and interactive web application that provides real-time weather data, air quality index, and hourly forecasts using city input or your current location. Designed for usability and smart forecasting.

Tools & Technologies:
HTML, CSS, JavaScript: Built a responsive and visually appealing frontend with animated transitions and interactive features.
OpenWeatherMap API: Used to fetch live weather, AQI, and forecast data.
Dynamic UI Features: Includes sun/moon switching, background changes based on weather, and animated splash screen.
Responsive Design: Optimized for both desktop and mobile views with media queries and adaptive layouts.

๐Ÿ‘€ View Project

๐Ÿšข ๐“ฃ๐“ฒ๐“ฝ๐“ช๐“ท๐“ฒ๐“ฌ-๐“ข๐“พ๐“ป๐“ฟ๐“ฒ๐“ฟ๐“ช๐“ต-๐“Ÿ๐“ป๐“ฎ๐“ญ๐“ฒ๐“ฌ๐“ฝ๐“ฒ๐“ธ๐“ท

Description: This project predicts passenger survival on the Titanic using ML models like Logistic Regression and Random Forest. Data preprocessing, feature engineering, and model evaluation are done using Python and scikit-learn.

Tools & Technologies:
Python Libraries: Used libraries like pandas, numpy, matplotlib, and seaborn for data loading, analysis, and visualization.
Scikit-learn Models: Applied Logistic Regression, Random Forest, and Decision Tree classifiers to predict passenger survival.
StandardScaler and LabelEncoder: Used for scaling numerical features and encoding categorical data such as gender and embarkation port.
Jupyter Notebook/Google Colab: Used as the development environment for writing, testing, and visualizing model performance.

๐Ÿ‘€ View Project

๐Ÿ“ ๐“—๐“ช๐“ท๐“ญ๐”€๐“ป๐“ฒ๐“ฝ๐“ฝ๐“ฎ๐“ท ๐“’๐“ฑ๐“ช๐“ป๐“ช๐“ฌ๐“ฝ๐“ฎ๐“ป ๐“ก๐“ฎ๐“ฌ๐“ธ๐“ฐ๐“ท๐“ฒ๐“ฝ๐“ฒ๐“ธ๐“ท

Description: A machine learning model to recognize handwritten characters (A-Z, 0-9) using image processing and neural networks for text digitization and analysis.

Tools & Technologies:
TensorFlow/Keras :for building and training the CNN model for image classification.
Python libraries : Libaries like NumPy, Pandas, Matplotlib, and OpenCV for data manipulation, visualization, and image processing.
MNIST and a custom A-Z handwritten letter dataset: For training and evaluation

๐Ÿ‘€ View Project

๐Ÿ’ณ ๐“’๐“ป๐“ฎ๐“ญ๐“ฒ๐“ฝ-๐“ข๐“ฌ๐“ธ๐“ป๐“ฒ๐“ท๐“ฐ-๐“œ๐“ธ๐“ญ๐“ฎ๐“ต

Description: This project aims to build a machine learning credit scoring model. It will predict loan repayment likelihood to help financial institutions make better lending decisions and minimize risks.

Tools & Technologies: Python Libraries -libraries like pandas, NumPy, and scikit-learn for data handling, preprocessing, and machine learning.
Random Forest Classifier - an ensemble learning method for classification.
Evaluation Metrics -metrics like accuracy, classification report, and confusion matrix to assess model performance.

๐Ÿ‘€ View Project

๐ŸŒ† ๐“ข๐“ถ๐“ช๐“ป๐“ฝ ๐“’๐“ฒ๐“ฝ๐”‚ ๐“œ๐“พ๐“ต๐“ฝ๐“ฒ-๐“๐“ฐ๐“ฎ๐“ท๐“ฝ ๐“ข๐“ฒ๐“ถ๐“พ๐“ต๐“ช๐“ฝ๐“ฒ๐“ธ๐“ท

Description: This project focuses on developing a Smart City Multi-Agent Simulation using agentic AI frameworks. The goal is to simulate urban scenarios like traffic control, energy management, emergency response, and healthcare using intelligent agents that coordinate and make autonomous decisions to optimize city operations.

Tools & Technologies: Python Libraries -Libraries like pandas, NumPy, and matplotlib for data handling and visualization.
Agentic AI Frameworks: Used AutoGen and CrewAI to design, build, and coordinate multiple specialized agents.
Technologies: Applied Generative AI, Machine Learning, and NLP techniques to enable agents to understand, learn, and respond dynamically.
Simulation & Evaluation: Tested multi-agent interactions in smart city scenarios and evaluated efficiency, coordination, and real-time responsiveness.

๐Ÿ‘€ View Project

๐ŸŒบ ๐“˜๐“ก๐“˜๐“ข-๐“ฌ๐“ต๐“ช๐“ผ๐“ผ๐“ฒ๐“ฏ๐“ฒ๐“ฌ๐“ช๐“ฝ๐“ฒ๐“ธ๐“ท

Description: This project uses ML models like Logistic Regression and Decision Tree to classify iris flowers into three species (Setosa, Versicolor, Virginica) based on sepal and petal measurements. Implemented in Python using scikit-learn.

Tools & Technologies:
Python Libraries: Used libraries like pandas, NumPy, matplotlib, seaborn, and scikit-learn for data handling, visualization, and machine learning.
Classification Models: Applied models like Logistic Regression, Decision Tree, and Random Forest for predicting iris species.
Evaluation Metrics: Used accuracy, classification report, and confusion matrix to measure model performance.
Jupyter Notebook/Google Colab: Used for writing, testing, and visualizing the model in an interactive environment.

๐Ÿ‘€ View Project

๐Ÿ  ๐“—๐“ธ๐“พ๐“ผ๐“ฒ๐“ท๐“ฐ-๐“น๐“ป๐“ฎ๐“ญ๐“ฒ๐“ฌ๐“ฝ๐“ฒ๐“ธ๐“ท ๐“พ๐“ผ๐“ฒ๐“ท๐“ฐ ๐“œ๐“›

Description: This project uses the XGBoost regression model to predict house prices based on features like area, location, number of rooms, etc. Built with Python, Pandas, and Scikit-learn.

Tools & Technologies:
Python Libraries: Used libraries like pandas, NumPy, matplotlib, and scikit-learn for data preprocessing, visualization, and model building.
XGBoost Regressor: Applied this powerful gradient boosting algorithm for accurate house price prediction.
Evaluation Metrics: Used metrics like RMSE (Root Mean Squared Error) and Rยฒ Score to evaluate model performance.
Jupyter Notebook/Google Colab: Used as the coding environment for implementation and testing.

๐Ÿ‘€ View Project

๐Ÿ“ท ๐“˜๐“ถ๐“ช๐“ฐ๐“ฎ ๐“ฝ๐“ธ ๐“˜๐“ถ๐“ช๐“ฐ๐“ฎ ๐“ฃ๐“ป๐“ช๐“ท๐“ผ๐“ต๐“ช๐“ฝ๐“ฒ๐“ธ๐“ท ๐”€๐“ฒ๐“ฝ๐“ฑ ๐“’๐“–๐“๐“

Description:This script implements a Pix2Pix Generative Adversarial Network (GAN) for image-to-image translation, specifically for the "facades" dataset, aiming to generate architectural facades from input images.

Tools & Technologies:
Python Libraries:libraries like os, pathlib, time, datetime, matplotlib, and IPython.display for file handling, time tracking, plotting, and displaying outputs. TensorFlow and Keras : used as the deep learning framework for building and training the Generator and Discriminator models.
Dataset : The "facades" dataset, accessed via a URL and tf.keras.utils.get_file, provides the training and testing images for the image translation task.

๐Ÿ‘€ View Project

๐Ÿ’ฌ ๐“ฃ๐“ฎ๐”๐“ฝ ๐“–๐“ฎ๐“ท๐“ฎ๐“ป๐“ช๐“ฝ๐“ฒ๐“ธ๐“ท ๐“ฆ๐“ฒ๐“ฝ๐“ฑ ๐“œ๐“ช๐“ป๐“ด๐“ธ๐“ฟ ๐“’๐“ฑ๐“ช๐“ฒ๐“ท๐“ผ

Description:This is a Markov Chain text generator. It learns word sequences from training text and then generates new text based on these learned probabilities.

Tools & Technologies:
Python:The primary programming language in which the Markov Chain text generator is implemented.. collections.defaultdict : A dictionary-like structure from Python's collections module used to store the Markov Chain model, where each key (a sequence of words) automatically gets an empty list as its value if the key is not yet present.
random : A built-in Python module used for introducing randomness in the text generation process, specifically for selecting the next word from the possible following words based on the learned probabilities in the Markov Chain.

๐Ÿ‘€ View Project

๐Ÿ†๐˜พ๐™€๐™๐™๐™„๐™๐™„๐˜พ๐˜ผ๐™๐™„๐™Š๐™‰๐™Ž๐Ÿ†

CERTIFICATION logo

๐Ÿ… ๐™Ž๐™ฉ๐™ช๐™™๐™ฎ ๐˜ผ๐™—๐™ง๐™ค๐™–๐™™ ๐™‹๐™ง๐™ค๐™œ๐™ง๐™–๐™ข ๐™–๐™ฉ ๐™‰๐™๐™Ž (๐™‰๐™–๐™ฉ๐™ž๐™ค๐™ฃ๐™–๐™ก ๐™๐™ฃ๐™ž๐™ซ๐™š๐™ง๐™จ๐™ž๐™ฉ๐™ฎ ๐™Š๐™› ๐™Ž๐™ž๐™ฃ๐™œ๐™–๐™ฅ๐™ค๐™ง๐™š)

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐˜ฝ๐™ช๐™ž๐™จ๐™ฃ๐™š๐™จ๐™จ ๐˜ฟ๐™–๐™ฉ๐™– ๐™‘๐™ž๐™จ๐™ช๐™–๐™ก๐™ž๐™จ๐™–๐™ฉ๐™ž๐™ค๐™ฃ

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐™‚๐™š๐™ฃ ๐˜ผ๐™„ ๐™‹๐™ค๐™ฌ๐™š๐™ง๐™š๐™™ ๐˜ฟ๐™–๐™ฉ๐™– ๐˜ผ๐™ฃ๐™–๐™ก๐™ฎ๐™ฉ๐™ž๐™˜๐™จ ๐™…๐™ค๐™— ๐™Ž๐™ž๐™ข๐™ช๐™ก๐™–๐™ฉ๐™ž๐™ค๐™ฃ

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐˜ผ๐™˜๐™˜๐™š๐™ฃ๐™ฉ๐™ช๐™ง๐™š ๐™Ž๐™ค๐™›๐™ฉ๐™ฌ๐™–๐™ง๐™š ๐™€๐™ฃ๐™œ๐™ž๐™ฃ๐™š๐™š๐™ง๐™ž๐™ฃ๐™œ ๐™…๐™ค๐™— ๐™Ž๐™ž๐™ข๐™ช๐™ก๐™–๐™ฉ๐™ž๐™ค๐™ฃ

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐˜ผ๐™˜๐™˜๐™š๐™ฃ๐™ฉ๐™ช๐™ง๐™š ๐™๐™š๐™˜๐™๐™ฃ๐™ค๐™ก๐™ค๐™œ๐™ฎ ๐˜พ๐™ค๐™ฃ๐™จ๐™ช๐™ก๐™ฉ๐™ž๐™ฃ๐™œ ๐™…๐™ค๐™— ๐™Ž๐™ž๐™ข๐™ช๐™ก๐™–๐™ฉ๐™ž๐™ค๐™ฃ

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐˜ผ๐™’๐™Ž ๐™จ๐™ค๐™ก๐™ช๐™ฉ๐™ž๐™ค๐™ฃ ๐˜ผ๐™ง๐™˜๐™๐™ž๐™ฉ๐™š๐™˜๐™ฉ๐™ช๐™ง๐™š ๐™…๐™ค๐™— ๐™Ž๐™ž๐™ข๐™ช๐™ก๐™–๐™ฉ๐™ž๐™ค๐™ฃ

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐˜ฝ๐™ง๐™ž๐™ฉ๐™ž๐™จ๐™ ๐˜ผ๐™ž๐™ง๐™ฌ๐™–๐™ฎ๐™จ ๐˜ฟ๐™–๐™ฉ๐™– ๐™Ž๐™˜๐™ž๐™š๐™ฃ๐™˜๐™š ๐™…๐™ค๐™— ๐™Ž๐™ž๐™ข๐™ช๐™ก๐™–๐™ฉ๐™ž๐™ค๐™ฃ

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐™ก๐™ก๐™ฎ๐™ค๐™™๐™จ ๐™—๐™–๐™ฃ๐™  ๐™œ๐™ง๐™ค๐™ช๐™ฅ ๐˜ฟ๐™–๐™ฉ๐™– ๐™Ž๐™˜๐™ž๐™š๐™ฃ๐™˜๐™š ๐™…๐™ค๐™— ๐™Ž๐™ž๐™ข๐™ช๐™ก๐™–๐™ฉ๐™ž๐™ค๐™ฃ

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐˜ฝ๐˜พ๐™‚๐™ญ ๐˜ฟ๐™–๐™ฉ๐™– ๐™Ž๐™˜๐™ž๐™š๐™ฃ๐™˜๐™š ๐™…๐™ค๐™— ๐™Ž๐™ž๐™ข๐™ช๐™ก๐™–๐™ฉ๐™ž๐™ค๐™ฃ

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐˜ฝ๐˜พ๐™‚๐™“ ๐™‚๐™š๐™ฃ ๐˜ผ๐™„ ๐™Ÿ๐™ค๐™— ๐™จ๐™ž๐™ข๐™ช๐™ก๐™–๐™ฉ๐™ž๐™ค๐™ฃs

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐˜พ๐™ฎ๐™—๐™š๐™ง๐™จ๐™š๐™˜๐™ช๐™ง๐™ž๐™ฉ๐™ฎ ๐™–๐™ฃ๐™™ ๐™„๐™Š๐™

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐˜ผ๐™ง๐™ฉ๐™ž๐™›๐™ž๐™˜๐™–๐™ก ๐™„๐™ฃ๐™ฉ๐™š๐™ก๐™ก๐™ž๐™œ๐™š๐™ฃ๐™˜๐™š ๐™–๐™ฃ๐™™ ๐™‡๐™š๐™œ๐™–๐™ก ๐™„๐™จ๐™จ๐™ช๐™š๐™จ

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐ˆ๐ง๐ญ๐ซ๐จ๐๐ฎ๐œ๐ญ๐ข๐จ๐ง ๐ญ๐จ ๐Œ๐š๐œ๐ก๐ข๐ง๐ž ๐‹๐ž๐š๐ซ๐ง๐ข๐ง๐  ๐จ๐ง ๐€๐–๐’

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐˜ผ๐™ง๐™ฉ๐™ž๐™›๐™ž๐™˜๐™ž๐™–๐™ก ๐™„๐™ฃ๐™ฉ๐™š๐™ก๐™ก๐™ž๐™œ๐™š๐™ฃ๐™˜๐™š ๐™›๐™ค๐™ง ๐™€๐™ซ๐™š๐™ง๐™ฎ๐™ค๐™ฃ๐™š

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐˜ฟ๐™š๐™ก๐™ค๐™ž๐™ฉ๐™ฉ๐™š ๐˜ฟ๐™–๐™ฉ๐™– ๐˜ผ๐™ฃ๐™–๐™ก๐™ฎ๐™ฉ๐™ž๐™˜๐™จ

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐˜ฟ๐™š๐™ก๐™ค๐™ž๐™ฉ๐™ฉ๐™š ๐˜พ๐™ฎ๐™—๐™š๐™ง ๐™…๐™ค๐™— ๐™Ž๐™ž๐™ข๐™ช๐™ก๐™–๐™ฉ๐™ž๐™ค๐™ฃ

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐˜ฟ๐™š๐™ก๐™ค๐™ž๐™ฉ๐™ฉ๐™š ๐™๐™š๐™˜๐™๐™ฃ๐™ค๐™ก๐™ค๐™œ๐™ฎ ๐™…๐™ค๐™— ๐™Ž๐™ž๐™ข๐™ช๐™ก๐™–๐™ฉ๐™ž๐™ค๐™ฃ

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–
CERTIFICATION logo

๐Ÿ… ๐™„๐™ฃ๐™ฉ๐™ง๐™ค๐™™๐™ช๐™˜๐™ฉ๐™ž๐™ค๐™ฃ ๐™๐™ค ๐˜พ๐™ฎ๐™—๐™š๐™ง ๐˜ผ๐™ฉ๐™ฉ๐™–๐™˜๐™ ๐™จ

๐Ÿ‘€ ๐•๐•š๐•–๐•จ โ„‚๐•–๐•ฃ๐•ฅ๐•š๐•—๐•š๐•”๐•’๐•ฅ๐•–

๐Ÿ“ž ๐˜พ๐™ค๐™ฃ๐™ฉ๐™–๐™˜๐™ฉ ๐™ˆ๐™š