Siddique0786-portfolio

About

Learn more about me

Data Science , Machine Learning & Deep Learning

I am Md Chand Hussain Siddique, a 23-year-old final year student pursuing B-Tech in Computer Engineering from I K G Punjab Technical University, Jalandhar ,Punjab. I hail from Gopalganj , Bihar, India. I am passionate about data science ,machine learning and Deep Learning.

Birthday: 04 Jul 2001
Phone: +919523928580
City: Gopalganj Bihar , India

Degree: Undergraduate
Email: chandhussain012@gmail.com
Freelance: Available

I am skilled in Python, data science, machine learning, deep learning, and web development, including HTML, CSS, React.js and Bootstrap. In databases, I have learned about MongoDB, SQL queries, and MySQL server. I also have knowledge of Power BI, data analysis, and data visualization. My ability to provide efficient and accurate assistance, along with my knack for generating helpful responses quickly, can greatly enhance productivity and support various team tasks.

Skills

HTML 100%

CSS 90%

Python 75%

Data Science 80%

ML and Dl 80%

SQL 70%

Resume

Check My Resume

Summary

Md Chand Hussain Siddique

As a newly minted Data Science, Machine Learning, and Deep Learning Engineer, I bring a fresh perspective and a hunger to learn. Although I'm just starting out, I possess a solid foundation in these fields through coursework and self-directed study. I'm eager to apply my theoretical knowledge to practical projects and collaborate with experienced professionals to develop innovative solutions. My enthusiasm, coupled with a knack for problem-solving and attention to detail, positions me well to tackle challenges head-on and contribute effectively to the team.

Gopalganj , Bihar , India , 841409
+919523928580
chandhussain012@gmail.com

Education

Bachelor of Technology & Computer Engineering

2020 - 2024

I.K. Gujral Punjab Technical University(Main Campus), Jalandhar, Punjab , 144603

CGPA - 7.5

Intermediate & Bihar School Examination Board

2019 - 2020

Sureshwari Devi Uchch Madhyamic Vidyalaya,Naviganj(Tapovan),Chapra,Bihar

Score - 68.6%

Relevant Courses

Python for Data Science and Machine Learning Bootcamp

Udemy Course

Statistics for Data Science and Business Analysis

Udemy Course

Tata Four week summer Training

Tata

Projects

Real Estate Price Prediction

Dec - 2023

Uses of Numpy, Pandas, Matplotlib, Scikit-learn, Linear Regression, Lasso Regression, Decision Tree, HTML, CSS, Pickle, Flask, JavaScript, Data Cleaning, Data Visualization these are used perfectly for this project.
Developed machine learning models using Python and Scikit-learn. Utilized linear regression with Accuracy Score of zero point eighty-two, decision tree with an accuracy score of zero point seventy-one, and lasso regression algorithms with an accuracy score of zero point sixty-eight and optimized real estate price prediction, resulting in enhanced model accuracy.
Contributed to Business Objectives to provide one hundred percent key insights for strategic decision-making in the real estate market. Facilitated better-informed investment and pricing strategies.

Potato Disease Classification

Dec-2023

Applied TensorFlow, Matplotlib, NumPy, and ImageDataGenerator for robust deep learning. And advanced data augmentation techniques to enhance dataset diversity and reduce overfitting for model building.
Achieved impressive results using CNN deep learning techniques. Training accuracy: percent, Validation accuracy: percent. Loss: during training, Loss: during validation.
Using fastAPI for building a robust web API. CORS Middleware for seamless handling of cross-origin resource sharing. PIL (Python Imaging Library) for efficient image processing. Uvicorn for ASGI (Asynchronous Server Gateway Interface) support.

Object Detection

Sep-2023

Using Python, cv2, cvzone, ultralytics, and YOLO to develop a robust computer vision system, resulting in a reduction in false positive rates and an improvement in overall detection accuracy.
Collaborated to fine-tune the YOLO model for improved accuracy, which resulted in an average detection rate across various image and video datasets.
Implemented real-time object identification, with an average processing time of seconds per frame.