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Hi there, I'm DevaπŸ‘‹

πŸš€ About Me

I am a recent graduate with a passion for Data Science, Machine Learning, and Artificial Intelligence engineering. I am certified as a TensorFlow Developer. I am eager to apply my academic knowledge and practical skills to real-world problems, aiming to make a meaningful impact through innovative solutions. I'm looking for my first job in the field of Data Science, Machine Learning, or AI engineering.

πŸŽ“ Education

  • Bachelor of Informatics Engineering
    Dian Nuswantoro University, 2024. Graduated with a GPA of 3.82, completing the program in 3.5 years with honors (Cum Laude).

πŸ“œ Thesis

  • Title: Emotion Recognition From E-Commerce Customer Reviews Using Transformer-Based Deep Learning
  • Description: This research explores the application of a Transformer-based deep learning architecture to identify emotions from customer reviews in Indonesian-language e-commerce. Using a dataset of 5,400 customer reviews, the model is designed to classify five categories of emotions: Happy, Sadness, Anger, Love, and Fear.
  • Technologies Used: Python, Pandas, Numpy, TensorFlow, Keras, Google Colaboratory, Streamlit.
  • Link to Project & Thesis: https://github.com/devapratama/text-emotion-recognition

πŸ”­ Projects

Here are a few highlights of the projects I have worked on:

1. Travel Customer Prediction

  • Description: This is the final project from the Kampus Merdeka independent study program at Rakamin Academy's Data Science Bootcamp 2023. I led a team of 7 members to successfully complete this project. The focus of this project was on analyzing customer data to provide actionable recommendations for policy makers and marketing teams. Additionally, we developed a predictive model to identify potential customers likely to purchase a newly introduced vacation package.
  • Technologies Used: Python, Pandas, Matplotlib, Scikit-learn, Google Colaboratory, Streamlit.
  • GitHub Repository: https://github.com/devapratama/travel-purchase-predictor

2. Skin Disease Classification

  • Description: This project focuses on Skin Disease Image Classification using transfer learning with DenseNet121. As part of the "SkinSight" team for the Bangkit 2023 Capstone Project, I contributed to the development of the machine learning model. Our goal was to accurately classify various skin diseases, leveraging advanced deep learning techniques to aid in early detection and diagnosis.
  • Technologies Used: Python, TensorFlow, Keras, Google Colaboratory, Flask.
  • GitHub Repository: https://github.com/devapratama/Skin-Disease-Classification

πŸ› οΈ Skills

  • Programming Languages: Python, SQL
  • Machine Learning: Scikit-Learn, TensorFlow, Keras
  • Data Analysis & Visualization: Pandas, NumPy, Matplotlib, Seaborn
  • Databases: MySQL, PostgreSQL
  • Tools & Platforms: Jupyter, Git, GitHub, Streamlit

πŸ’¬ Let's Connect

Ahmad Sabil Deva Pratama's Projects

home-credit-scorecard-model icon home-credit-scorecard-model

This project improves the credit scoring model for PT Home Credit Indonesia, using machine learning to accurately approve loans for creditworthy customers, thereby increasing financial inclusion and reducing defaults.

skin-disease-classification icon skin-disease-classification

Skin Disease Classification with DenseNet121. This is a project team for Bangkit Capstone Project named "SkinSight", and I'm part of creating the machine learning model. My Team: https://github.com/SkinSight-C23-PS059

spam-sms-classifier icon spam-sms-classifier

This project develops a machine learning model to classify SMS messages as spam or legitimate, using NLP techniques to enhance user experience by filtering out unwanted messages.

text-emotion-recognition icon text-emotion-recognition

This project explores the application of Transformer-based deep learning architecture to identify emotions from customer reviews in Indonesian-language e-commerce.

travel-purchase-predictor icon travel-purchase-predictor

Travel-Customer-Prediction project aims to predict which customers are likely to purchase a new "Wellness Tourism Package" using machine learning. By analyzing customer data from a Kaggle dataset, the project provides insights to improve marketing strategies and target potential buyers more effectively.

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