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Name: Hardik Sankhla

Type: User

Company: JIET Institute of Design & Technology

Bio: Data Scientist | Artificial Intelligence & Machine Learning Engineer | Data Analyst | Business Intelligence Analyst | NLP | DL(ANN & CNN)

Location: Jodhpur, Rajasthan

Hi there, I'm Hardik Sankhla 👋

Enthusiastic Data Science Student | AWS & Frontend Developer | Open Source Contributor

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👨‍💻 About Me

  • 🎓 Data Science student at JIET Institute of Design and Technology
  • 💼 Skilled in Data Analytics, Visualization, ML, and Cloud Computing
  • 💡 Eager to contribute innovative solutions in both Data Science and Cloud domains
  • 🌱 Currently learning advanced concepts in Deep Learning

🚀 Skills

Languages

Python C C++ SQL JavaScript HTML CSS

Frameworks and Libraries

Flask Django Bootstrap

DevOps and Tools

Git Docker Kubernetes AWS

Cloud and Security

AWS Firebase Linux

Data Visualization

Power BI Tableau Seaborn Matplotlib Plotly

Others

  • Data Modeling
  • Design Patterns
  • Software Engineering
  • Machine Learning
  • NLP
  • Deep Learning (ANN, CNN)
  • Computer Vision

🔭 Internships

AWS Cloud Computing Intern | LinuxWorld Informatics Pvt Ltd (Jul 2023 – Sep 2023)

  • Proficient with AWS CLI and managing data efficiently through snapshots
  • Explored AWS storage solutions like EBS, S3, and EFS
  • Learned monitoring, scaling, security, and automation techniques
  • Worked with AWS AI services: Rekognition, Comprehend, Transcribe, Translate, Forecast

Frontend Developer | SEED Infotech Ltd (May 2023 – Jun 2023)

  • Developed responsive front ends using HTML, CSS, Bootstrap, and JavaScript
  • Focused on optimizing user engagement and satisfaction through compelling pages

🎓 Education

  • B.Tech in Data Science (CSE) at JIET Institute of Design and Technology (Aug 2022 – 2025)
  • Class 12th at Mahatma Gandhi Govt School, Jodhpur (GPA: 87.90, 2020 – 2021)
  • Class 10th at Laxmi Devi Mundra Public School, Jodhpur (CGPA: 58, 2018 – 2019)

🚀 Projects

Dog Breed Prediction | Keras, TensorFlow, Kaggle, CNN (Nov 12, 2022)

  • Built, trained, and tested a Convolutional Neural Network to identify dog breeds
  • Utilized Kaggle API for dataset acquisition and data preprocessing
  • Implemented a CNN architecture and achieved satisfactory accuracy on the test set

📊 GitHub Stats

Top Languages

GitHub Stats

GitHub Streak

📫 Let's Connect!

Feel free to reach out to me via Email or LinkedIn.

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Hardik Sankhla 's Projects

analysis-of-fifa-2019 icon analysis-of-fifa-2019

This project aims at complete analysis of FIFA 2019 using Python. The Data Set used is : FIFA 19 dataset of Electronics Arts Vancouver #### We will perform a complete Exploratory Data Analysis of the FIFA 2019. We will be observing the best players, bets teams, we will make our best teams and try to access players on the basis of there ratings.

dog-breed-prediction icon dog-breed-prediction

In this project, we will see how to use Keras and TensorFlow to build, train, and test a Convolutional Neural Network capable of identifying the breed of a dog in a supplied image. This is a supervised learning problem, specifically a multiclass classification problem.

message-using-python icon message-using-python

Send SMS to Multiple Users - Python Code! 💬📱 You Can send personalized messages to multiple recipients effortlessly.. 🚀 With this Python code, powered by Twilio's SMS gateway service, you can easily send text messages to multiple users simultaneously! 📨📲#PythonSMS #TwilioCode #SMSBroadcast #PythonProgramming #TextMessaging #BulkSMS #TwilioAPI

pan-card-tampering-detection icon pan-card-tampering-detection

Tampering of PAN card using computer vision. This project will help different organization in detecting whether the Id i.e. the PAN card provided to them by thier employees or customers or anyone is original or not.

traffic_signal_classification icon traffic_signal_classification

In this project, we will use Convolutional Neural Network to build train and test a traffic sign classification model. We will build this model using tensorflow and keras. It is a multiclass classification problem. This model can be used to make smarter cars.

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