Machine Learning Computer Science

SAMREEN MOHAMMED

COMPUTER SCIENCE ENGINEER

C O NTACT

*-*-***/***/* C.E COLONY

BAGH AMBERPET, HYDERABAD 500013

EMAIL: ****************@*****.***

MOBILE: +91-964*******

PROFESSIONAL SUMMARY

I am a motivated Computer Science graduate skilled in C, Java, and Python. Strong analytical abilities with experience in software development. Seeking a dynamic role to apply technical expertise and problem-solving skills. E DUCATION

B. TECH: C.S.E

SHADAN WOMEN'S COLLEGE OF ENGINEERING AND

TECHNOLOGY

JAWAHARLAL NEHRU TECHNOLOGICAL

UNIVERSITY MAY 2024

INTERMEDIATE

NARAYANA JUNIOR COLLEGE

TELANGANA BOARD OF INTERMEDIATE

EDUCATION MAY 2018

SSC

DIVYANJALI HIGH SCHOOL

STATE BOARD OF SECONDARY EDUCATION MAY 2016

KEY SKILLS

C

J A V A

P Y T HON

MS O F F I CE

CERTIFICATIONS

• C Language: Certification showcasing proficiency in programming fundamentals, syntax, and problem-solving using the C programming language.

• Java Programming: Certification recognizing understanding of core Java concepts, including object-oriented programming, exception handling, and multithreading.

• Python Programming: Certification highlighting adeptness in Python programming, covering various libraries and frameworks useful in data analysis, web development, and machine learning.

T ECHNICAL PROJECTS

Provable Data Possession with Outsourced Data Transfer

• Developed a system to ensure data integrity when stored in untrusted environments.

• Utilized the Dynamic Provable Data Possession (DT-PDP) method, which checks the integrity of stored data by calculating and verifying proofs for every block of data. This project demonstrates an understanding of security measures in data management and the importance of maintaining data integrity.

• Technology Used: Designed using Machine Learning techniques, enabling intelligent processing of data integrity checks and analysis. Prediction of Election Using Sentiment Analysis and Neural Network

• Implemented a predictive model to analyze and forecast election outcomes based on public sentiment extracted from Twitter data.

• Utilized natural language processing techniques to gather and analyze sentiments across different geographical regions. This project highlights the intersection of data science and political analysis.

• Technology Used: Utilized Deep Learning frameworks for building neural networks, specifically focusing on LSTM (Long Short-Term Memory) networks for effective sentiment analysis, showcasing proficiency in advanced data analytics and predictive modeling.

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